Top Companies Hiring for Data Engineers in 2025: Fall Update
Fall 2025 is a fantastic time to be a data engineer. After a rollercoaster few years, demand for data engineering talent is surging again. The biggest names in tech – and even some old-school giants – are scrambling to fill data roles to power their next wave of innovation. But which employers are leading the charge, and what can you expect if you’re aiming to join their ranks? In this funnel-style update, we’ll break down the top U.S. companies hiring data engineers, what they’re looking for, how much they pay, and how you can position yourself to land those coveted offers.
Key Takeaways:
- Big Tech is back in hiring mode: Companies like Amazon, Apple, Google, Microsoft, and Meta are aggressively hiring data engineers in Fall 2025, driven by huge AI and cloud initiatives. Even legacy tech firms (think IBM and Oracle) are ramping up recruitment of data pipeline specialists to modernize their platforms.
- Hiring stats are eye-opening: Apple alone has over 2,100 open engineering positions right now, with Amazon and IBM close behind – each near the ~1,800–2,000 mark in open roles. This reflects a broader industry trend: after the 2022–23 slowdown, tech giants are adding thousands of jobs for data and software engineers.
- High demand = high salaries: The average data engineer salary in the U.S. hovers around $130,000/year, but at top companies, total compensation often soars well above $200,000 for experienced talent. Base salary, bonus, and stock options together make for hefty compensation packages – and salary expectations have risen compared to last year.
- Experience matters: Many firms are seeking seasoned pros to lead critical projects. In fact, nearly half of current openings skew senior-level or above, which means mid-career data engineers are especially sought after. (Entry-level roles exist, too, but companies are keen on candidates who can hit the ground running.)
- In-demand roles and skills: It’s not just generic “Data Engineer” titles. Streaming data engineers (for real-time pipelines), data platform engineers, and cloud data specialists are hot roles in 2025. Employers want strong data engineering skills in SQL, Python, Spark, Kafka, and cloud platforms (AWS, Azure, GCP) – plus a knack for handling big data for AI/ML projects.
- Multiple offers are on the table: Top candidates often juggle more than one job offer. With data engineers in high demand, you may find yourself with choices, which gives you leverage in negotiation. Companies are willing to negotiate on base salary, relocation, signing bonuses, and equity to lock in the right talent.
- Upskilling is key: Keeping your skills cutting-edge (think AI-ready data pipelines and real-time analytics) can significantly boost your job prospects. Building the right skill set not only helps you get hired faster but also ensures you can command a premium in this competitive market.
Now, let’s dive deeper into the state of the data engineering job market this fall and profile the top companies that are hiring like crazy.
Why Data Engineers Are in High Demand (Fall 2025)
It’s no secret: data engineers are one of the most in-demand tech roles right now. The Fall 2025 job market has flipped from the slowdown of a couple of years ago. What’s driving this surge? In a word: data (and lots of it). Organizations across the board are doubling down on data pipelines, cloud analytics, and AI initiatives. To do that successfully, they need armies of data engineers to build and maintain the infrastructure.
Post-pandemic data boom: Companies accumulated massive amounts of data during digital acceleration in recent years. Now they’re determined to do something with it – whether it’s training AI models, personalizing customer experiences, or streamlining operations. This means investing in robust data engineering teams to handle data ingestion, ETL pipelines, real-time streaming, and warehousing. If you can design a scalable pipeline or optimize a data warehouse, there’s a job (or five) for you out there.
AI gold rush: 2025 has been the year of generative AI in the enterprise. Tech firms, large and smal,l are racing to integrate AI features into their products. Behind every flashy AI demo is a ton of data prep work – and guess who does that? Data engineers. From feeding machine learning pipelines with training data to deploying real-time data streams for AI-driven apps, data engineers are the unsung heroes of the AI boom. Companies know this, and they’re hiring accordingly. (If you have experience handling big data for AI/ML, you’ve basically got a golden ticket in today’s market.)
Recovery from the hiring freeze: Let’s address the elephant in the room: the 2022–2023 tech layoffs and hiring freezes. Those were rough. But the tide has turned. Hiring is slowly but steadily rising across big tech as business confidence returns. Many organizations paused growth and are now playing catch-up on crucial roles. Software and data engineering postings have been ticking up throughout 2024, and by Fall 2025, we’re seeing significant momentum.
In fact, recent industry data shows a steady rise in tech vacancies in the past year – a clear sign that the market is rebounding. Companies that over-cut are now finding they need talent yesterday. The result? A lot of open chairs are waiting for skilled data engineers to fill them.
Companies with the most open software engineering positions as of September 2025. Data shows Big Tech (Apple, IBM, Amazon) leading in job openings, which correlates with many data engineering opportunities at these firms.
Notice the chart above: Apple, IBM, Amazon, Oracle, and others top the list for open engineering roles. That aligns with what we’re about to explore – these same players are also among the top companies hiring data engineers this fall. It’s a good visualization of just how many positions are out there (Apple alone lists over 2,100 open tech jobs!). For you as a job seeker, this means plenty of opportunities – especially if you target the right companies.
Upward salary trends: With high demand comes higher pay. We’re seeing a shift in salary ranges compared to a year ago. For example, a year or two back, an entry-level data engineer might have been offered $90k; now it’s common to see offers well into six figures even for juniors in tech hubs. And mid-level roles that used to top out around $150k total are now edging into the $160–$180k range at many companies. The salary range is trending up, and top-tier employers are willing to pay a premium for the right skills. This is great news if you’re job hunting – but it also means you should know your worth and be prepared to negotiate (we’ll talk more about compensation later on).
Speaking of negotiation, another dynamic of a hot job market is that experienced engineers often have multiple options. Companies know this, so they’re sweetening deals – higher base salaries, juicy signing bonuses, more stock – to win over candidates. Don’t be afraid to compare offers and politely leverage one against another. It’s not being greedy; it’s standard practice when you have in-demand skills. A little savvy negotiation can bump your compensation package significantly. More on that in the salary section, but keep it in mind as we go through who’s hiring.
Alright, let’s get to the heart of the matter: Which companies are on a hiring spree for data engineers right now? Below, we profile the top U.S. companies (from tech giants to rising stars) that are filling data engineering roles by the dozens. For each, we’ll cover what they’re up to, what roles they need, and what they tend to pay. You’ll notice some common themes (cloud and AI everywhere!), but each company has its own flavor in how it approaches data engineering.
Top Companies Hiring Data Engineers (Fall 2025)
Amazon – King of Scale and Constantly Hiring
It’s no surprise that Amazon is near the top of any tech hiring list. This fall, Amazon continues to hire data engineers at a staggering rate across its many businesses. From Amazon Web Services (AWS) to e-commerce, logistics, advertising, and even Alexa, Amazon’s appetite for data talent is huge. On Amazon’s careers site, you’ll find hundreds upon hundreds of data engineering openings – everyone from new grads (Data Engineer I) to seasoned principal engineers.
Why so many roles? Amazon runs on data. They need engineers to build pipelines for everything: customer purchase data, inventory and supply chain data, video streaming metrics for Prime Video, you name it. AWS, in particular, is a playground for data engineers; teams there develop big data services (like Kinesis, Redshift, and Glue) and also use those tools internally. If working on a massive-scale data infrastructure excites you, AWS or any core Amazon team is the place to be. Even Amazon’s Last Mile delivery science group is hiring data pipeline specialists to optimize routes and delivery times. It’s data engineering on a truly global scale.
Hiring trends: Amazon went through a hiring freeze and some cuts in 2023, but by 2025, it had refocused on growth. Many AWS teams that slowed down are now back to aggressively recruiting as cloud demand picks up. The company has well over 1,000 data engineer job postings live (indeed, possibly around ~1.7k roles on job boards at the moment). They are especially targeting engineers with AWS cloud expertise (naturally), as well as those who can handle real-time streaming data (Kafka/Kinesis) and build scalable ETL on their platforms.
Compensation at Amazon: Amazon is known for its unique compensation structure. They historically had a base salary cap, but that’s been raised, so base salaries for data engineers can go into the low-to-mid six figures now (a mid-level Data Engineer II might have a base around $130–150k). Amazon is generous with RSUs (stock grants) to make up a big part of your package, plus they often give signing bonuses for the first two years. A typical total compensation for an experienced data engineer (say L5 level) at Amazon is around ~$220k/year, split between base, stock, and a smaller annual bonus. At more senior levels (L6 and above), totals can hit $250k+ with significant stock upside – and if you climb to principal (L7+), you’re looking at very high packages, often with a large portion in Amazon stock that can appreciate.
One thing to note: Amazon’s hiring process is rigorous (multiple interviews, including a coding test and behavioral questions around their famous “Leadership Principles”). But they move fast and, if you fit, they’re not shy about extending an offer. Negotiation tip: Because Amazon’s offers rely heavily on stock, be sure you understand the current stock value and vesting schedule. The good news is Amazon’s stock has been on the rise, so those RSUs could be worth even more down the line. And yes, you can negotiate an Amazon offer – sometimes they’ll improve the signing bonus or initial stock grant if you have another offer in hand.
Apple – Doubling Down on Data (and AI)
Apple has quietly become one of the biggest tech employers of data engineers, which might surprise some folks. Traditionally known for hardware and mobile software, Apple in 2025 is investing heavily in services (like Apple TV+, Music, iCloud) and AI features (hello, Siri improvements!). All that requires heavy data lifting in the background. As a result, Apple’s hiring for data roles is robust. In fact, Apple currently has more open engineering positions than at any time in its history, and data engineering jobs are a significant chunk of those.
At Apple, data engineers find themselves working on a wide array of cool projects: Siri’s analytics and data pipelines, Apple’s push into health data (think Apple Watch metrics), retail and Apple Store analytics, and, of course, the infrastructure behind their services and App Store. Apple also has a growing cloud infrastructure effort (they’ve been quietly building out Apple Cloud services), which means more roles in data platform engineering to support that internal cloud.
What Apple looks for: Apple, being Apple, they value performance and optimization a lot. If you join as a data engineer, you’ll likely be dealing with high-volume, low-latency systems that need to run efficiently at scale. Strong SQL and Python skills are a must, and if you have a knack for optimizing Spark jobs or tuning databases, you’ll shine here. Apple also tends to appreciate a bit of domain expertise – e.g., if you work on Apple Music’s team, knowing something about music data or recommendation engines could help; on the health team, some exposure to healthcare data standards might be a plus. Essentially, they like well-rounded engineers who can blend software engineering rigor with data savvy.
Compensation at Apple: Apple pays competitively, though its structure is a little different from some other giants. Base salaries for data engineers are solid (often in the $120k–$160k range, depending on level and location). Apple typically gives annual bonuses (around 10% or so) and refreshes stock yearly, but initial stock grants for new hires might not be as sky-high as at Google or Meta. That said, total packages for mid-level folks can easily be in the $180k–$200k range, and senior engineers (ICT4/ICT5 levels in Apple’s system) can see $250k+ total. Apple’s stock has been very stable and strong, which makes its RSUs valuable. They’re also known for good benefits (including stock purchase discounts and, obviously, that shiny employee discount on Apple products).
Apple’s work culture is a bit more traditional – they’ve encouraged working on campus (in that spaceship campus, no less) more than some peers. But as a data engineer, you’d be at the heart of innovation that directly touches millions of users’ experiences. If you like the idea of your work helping improve an iPhone feature or an Apple service used globally, Apple would be a fulfilling place. Just be ready for high expectations; Apple’s motto of secrecy and perfection means they’ll expect you to deliver quality and keep things under wraps until launch day!
Google (Alphabet) – Data at Massive Scale and New AI Adventures
Google – the name is synonymous with big data. In Fall 2025, Google is once again hiring strongly for data engineering and related roles, after a period of caution. Google’s entire business is built on data: search indexing, YouTube streams, Ads analytics, Google Cloud – it’s a data playground. As a data engineer at Google (which is technically Alphabet, but everyone still says Google), you could work on anything from global data pipelines that process web indexing to internal analytics for Gmail/Photos, or building data tools on GCP (Google Cloud Platform) for external customers.
Current focus: Post-2023, Google has been all-in on AI, especially after the buzz around AI startups. Google is integrating generative AI into Search and Workspace, which means a lot of back-end data work: preparing training datasets, monitoring model outputs, and building the infrastructure to serve AI features to billions of users. The company is actively seeking data engineers to support its Google DeepMind and Google Research teams – roles where you’ll wrangle extremely large datasets for machine learning. If you have experience with ML pipelines or data for AI, Google will take a hard look at you.
Additionally, Google Cloud is a major growth area. They need data engineers internally to build out Google’s cloud data offerings (BigQuery, Dataflow, etc.) and also in customer-facing roles (like professional services data engineers who help big clients implement solutions on GCP). So whether you’re into developing the next gen of data platforms or applying them in real-world scenarios, Google likely has a spot that fits.
Work culture at Google: Google tends to hire smart generalists. They love strong CS fundamentals. As a data engineer, you’re expected to code at a near software-engineer level (think solid algorithms, clean code, and system design) while also being an expert in data systems. They also value innovative thinking – part of your job may involve designing new ways to handle data at Google’s mind-boggling scale (we’re talking petabytes and beyond). One nice thing: Google often allows engineers to move between teams internally, so you could start in, say, Ads data infrastructure and later join a YouTube analytics team if that interests you.
Compensation at Google: Google is known for excellent pay and perks. A typical Google data engineer at an intermediate level (L4 or L5 in Google’s leveling) can expect a base salary in the $140k–$180k range, a significant annual bonus (15%+), and large stock grants. Google’s total compensation tends to be top-tier: mid-level total packages around $250k are common, and senior staff can go much higher ($300k, $400k+ depending on level and performance). For instance, an entry-level L3 Data Engineer might start around $120k base, but by the time you’re L5 (senior-ish), base could be ~$170k and total with bonus/stock around the mid-$200s. One thing about Google: they refresh your stock grants very generously if you perform well, so over time your package can grow significantly. Plus, the famous Google benefits (free food, etc.) are still there and sweeten the deal.
If you aim for Google, be prepared for a challenging interview process – you’ll face data structure and algorithm questions, SQL and distributed systems questions, and possibly some ML basics. It’s not easy, but once in, you’re at a company where virtually every data engineering problem is an interesting one. The scale and impact at Google are hard to match.
Microsoft – Azure, AI, and Everything Data
Microsoft has reinvented itself as a cloud and AI leader, and it’s hiring data engineers to match. Under CEO Satya Nadella’s leadership, Microsoft has been all about “cloud-first, AI-first” which is great news for data professionals. In 2025, Microsoft’s Azure cloud platform is growing rapidly, meaning lots of open roles for data engineers to build and maintain Azure’s data services. They’re also integrating OpenAI’s tech (thanks to that big partnership) across products like Bing, Office (Microsoft 365 Copilot), and more, which again requires robust data engineering behind the scenes.
Where you could fit in: Microsoft’s data engineering openings span many teams. Love cloud tech? Azure Data (think Azure Synapse, Data Factory, Cosmos DB) always needs engineers to improve those services. Prefer consumer products? Teams for Windows, Office, Xbox, and LinkedIn – they all have data pipeline needs for telemetry, user behavior data, etc. Microsoft also has a huge Business Intelligence and analytics operation internally (they drink their own champagne with Power BI dashboards for everything). So you might work on internal data warehousing that guides Microsoft’s decisions or on customer-facing cloud features that tens of thousands of companies will use on Azure.
Notably, Microsoft’s enterprise DNA means they value folks who understand building reliable, secure systems. Data engineers here often engage in data governance, security, and compliance issues (especially if you work on something like Azure, where customers care about data privacy). If you have experience in handling sensitive data or building compliant data pipelines (finance, healthcare industries, etc.), Microsoft will see that as a plus.
Compensation at Microsoft: Microsoft pays well, though generally slightly below the absolute top-of-market, like Google. However, they’ve been closing the gap in recent years. A data engineer at Microsoft in, say, the Seattle area (Redmond HQ) might have a base salary in the $130k–$150k range at the mid-career level (level 62 or 63 in their internal leveling). Total comp, including bonus (10-15%) and stock, could land around $180k–$200k. Senior roles (64, 65 levels) will push above $220k-$250k total, with a higher base and stock. Microsoft has a reputation for giving out consistent annual stock refreshes and sometimes bonuses called “golden tickets” for standout performers. They also adjusted salaries upward in 2022–2023 to retain talent, so new hires are benefiting from those bumps.
One nice thing: Microsoft often has geographically adjusted pay, but also many roles open in multiple locations (Seattle, Bay Area, also new hubs like Austin). So if you’re open to places like Texas (where the cost of living is lower), you might get a great salary that goes further. In fact, tech growth in Texas is big – Microsoft and others have been listing many jobs in Texas, tapping into the talent moving there.
Culturally, Microsoft has become a pretty agile place (gone are the inflexible old MS days). Data teams use modern tech stacks (lots of Azure, of course, but also open-source big data tools). There’s an emphasis on collaboration – many data engineering roles will have you working closely with PMs, data scientists, and customers. If the idea of empowering developers and enterprises with data tools appeals to you, Microsoft’s a top choice. And yes, they still have a good work-life balance relative to some other giants, which is a nice bonus.
Meta (Facebook) – Pivoting and (Quietly) Hiring for Data
Meta had a wild ride recently – after rebranding from Facebook and going big on the metaverse, they hit a rough patch with overstaffing and did some layoffs in 2023. However, by Fall 2025, Meta has stabilized and is selectively hiring data engineers again, focused on its core strengths and new bets in AI. They might not be hiring as many as Amazon or Apple at the moment (Meta has a few hundred open data/engineering roles, versus thousands at the others), but don’t let that fool you: Meta remains a top-tier destination for data engineers and still offers arguably the richest compensation packages in the industry.
What’s happening at Meta? The company has refocused on what it calls the “Family of Apps” (Facebook, Instagram, WhatsApp) and AI-driven features. Ads are still Meta’s money-maker, so data engineers are heavily needed in ads targeting and analytics teams – handling streams of click and impression data to optimize ad delivery in real time. Meta is also big on recommender systems (think Instagram feed, Facebook Watch, etc.); those require huge data pipelines to track user interactions and feed machine learning models that decide what content you see. If you work on a feed or recommendation team, expect to manage some of the largest data volumes imaginable, with sub-second latency requirements.
Then there’s Reality Labs (AR/VR) and the metaverse side – while scaled back, Meta hasn’t given up. Data roles there might involve analyzing how users interact in VR or building data tools for 3D content. And of course, Meta jumped on the generative AI trend to enhance things like AI chatbots, content generation, and moderating content. They have open data engineering roles in their AI research infrastructure teams (similar to Google, managing huge datasets to train Llama models and such). So opportunities abound if you’re an expert in managing unstructured big data or high-volume event streams.
Meta’s culture and expectations: Meta moves fast (“move fast” is literally a slogan). Data engineers at Meta often operate with a lot of autonomy – you are expected to identify problems and solve them, not wait for detailed instructions. You’ll likely use a mix of Meta’s internal tools (they have proprietary data query languages and frameworks, along with standard SQL, Python, etc.). One thing about Meta: they blur the line between software engineer and data engineer somewhat. Many data engineers have to be comfortable writing production-quality code (in Python or even C++ for some backend data services), and conversely, many backend engineers do data-heavy work. So, demonstrating strong coding skills and an ability to handle data at scale is key to landing a role here.
Compensation at Meta: Simply put, Meta pays a lot. A typical E5-level Data Engineer (which is like a senior engineer) could have a base salary around $160k–$180k, a target bonus of ~15%, and stock awards that bring total annual comp to around $250k–$300k. At the E6 (staff engineer) level, it’s not unusual for total comp to exceed $400k. Even junior-mid level (E4) might total around $180k+. Meta has historically given very large initial stock grants to sweeten offers – and their stock has done well in 2023–2025, so those grants are valuable. The company also does quarterly bonuses (instead of annual), which some folks like for cash flow.
If you get an offer from Meta, you should negotiate – they often leave wiggle room. They know they’re a top choice, but they also know you likely have other options, so politely asking if they can review the offer often results in a higher stock grant or signing bonus. It helps if you can cite another company’s offer; Meta hates losing candidates to competitors.
In summary, Meta in 2025 is a bit more mature and perhaps slightly less chaotic than the Facebook of a few years ago, but it’s still a place where a talented data engineer can have a huge impact – and be very well rewarded for it.
IBM – The Sleeping Giant Awakes (Hiring in Droves)
IBM, a 100+ year-old tech company, might not be the first name that comes to mind for cutting-edge data engineering. But guess what? IBM is hiring heavily for data roles in 2025, and they’re proving they still have skin in the game. In fact, industry reports show IBM’s valuation and business are the strongest they’ve been in ages, thanks to bets on cloud and AI paying off. For data engineers, IBM presents an interesting mix of working with both modern technologies and enterprise-scale legacy systems (sometimes in the same project!).
What IBM is up to: IBM’s big focus areas now are hybrid cloud, AI (Watsonx), and consulting services. They acquired Red Hat a few years back, so a lot of IBM roles involve building data platforms that span on-premise and cloud (Red Hat OpenShift Data Foundation, etc.). If you join IBM as a data engineer, you might be building solutions for clients – for example, setting up a data lake for a bank as part of IBM Consulting – or working on IBM’s own products like Cloud Pak for Data. IBM also has a strong presence in industries like finance, healthcare, and government. That means they need data engineers who understand those domains to help implement analytics and AI solutions for clients in a secure, compliant way.
One area of growth: AI and Watson. IBM was early to AI with Watson, and in 2025, they’ve revamped it (the new Watsonx platform). They are hiring data engineers to ingest and prepare industry-specific datasets that feed these AI models. Think training large language models on reams of domain text, or processing tons of medical images for AI – that’s heavy-duty data engineering.
Roles and skills at IBM: Because IBM does a lot of client-facing work, some data engineering jobs here blur into data consultant territory. You might spend time interacting with a client’s tech team, designing data pipelines that fit their needs, and then implementing and handing off. So, strong communication and the ability to translate business needs into technical data solutions are valued. Technical skills-wise, IBM still has legacy tech (DB2 databases, etc.) but is also all-in on open source and cloud – skills in Kubernetes, Hadoop/Spark, Kafka, and various DBs (SQL and NoSQL) are all useful. If you have familiarity with mainframe data or older ETL tools and also modern ones, you’ll be a unicorn (IBM still deals with a lot of Fortune 500 backends, some of which are quite old).
Compensation at IBM: Historically, IBM’s pay was a bit more modest compared to Silicon Valley darlings. That’s still somewhat true – you likely won’t get Google-level stock grants at IBM – but they have been bumping salaries to attract talent. For an IBM data engineer, expect a base salary roughly in the $100k–$130k range for early to mid career (they use band levels like 6, 7, 8… a Band 7 might be around $110k base). They do offer bonuses (maybe 5-10%) and some stock options or grants, but not massive amounts. The median total comp for an IBM data engineer is around $120k. Senior folks (Band 9 or 10, which are like lead/principal) can make $150k–$170k total.
While that’s lower than big tech, keep in mind IBM jobs can be more stable and 9-to-5 oriented. The culture is more traditional; you won’t necessarily be grinding long startup hours. There’s also the factor of location – IBM has offices and allows remote work across cheaper-cost areas, so your salary goes further if you’re not in NYC or SF. IBM’s benefits are solid, and they invest in employee training (you can take lots of courses, even pursue advanced degrees while working). So, if you value stability and working on large-scale enterprise projects, IBM is a great option. And hey, that IBM name on your resume still carries weight, especially in certain industries.
Oracle – Cloud Ambitions Fueling Hiring
Oracle is another veteran tech giant making waves in the data hiring scene. Best known for databases, Oracle has transformed into a cloud player (Oracle Cloud Infrastructure – OCI) and is leveraging its long-standing enterprise relationships to grow. In Fall 2025, Oracle has well over a thousand open positions in the U.S., and a good chunk of those are in data engineering, data platform management, and cloud data services roles.
Why Oracle is hiring data engineers: Oracle’s core business is all about data storage and management (they basically pioneered the modern database), so as they pivot to cloud and AI, they need fresh talent to build new data-driven features. OCI is competing with AWS/Azure/GCP, meaning they’re developing big data services, analytics platforms, and AI cloud offerings. If you join Oracle, you might work on teams developing cloud database services, data integration tools, or analytics dashboards for cloud customers.
Another side of Oracle is its suite of enterprise applications (ERP, CRM, etc.). They are embedding more analytics and AI into those products as well – for example, adding predictive analytics to Oracle Financials or using data pipelines to feed Oracle’s HR software insights. Data engineers here help design those product-embedded data pipelines, ensuring that raw data from customers gets processed into useful metrics and predictions inside the apps.
Oracle’s hiring profile: Oracle often looks for folks with strong database skills (no surprise). If you’re good with SQL optimization, data modeling, or have worked with Oracle Database or MySQL, that stands out. They also value cloud experience – even if it’s AWS or Azure experience, it translates to understanding OCI. Many job listings mention knowledge of data warehousing, ETL pipelines, and Python/Java. Security clearance can be a requirement for some Oracle roles, especially those involving government or defense (Oracle has an entire division focusing on Gov/Defense tech, where data engineers might need to be U.S. citizens with clearance to work on sensitive projects). So, for those coming from a defense background, Oracle could be a fit.
Compensation at Oracle: Oracle’s pay is fairly competitive mid-market. A typical data engineer might see a base salary in the low $100ks to mid $100ks. Based on reported figures, mid-level total compensation is around $150k–$170k. For instance, an IC3/IC4 (individual contributor level 3 or 4, which is like mid to senior) could have a base of ~$120k, a bonus of 10%, and some equity to total maybe $160k. Oracle isn’t known for huge equity grants to rank-and-file, but they do give performance bonuses and occasionally profit-sharing. They’ve also been known to provide some relocation assistance or housing stipends for roles in high-cost areas, which is a nice perk.
One interesting aspect: Oracle’s headquarters relocated to Austin, Texas, and they’ve been investing in hiring there and other lower-cost regions. So you might find that an Oracle offer in Texas feels quite generous for the locale. On the other hand, if you’re in Silicon Valley, Oracle’s pay might feel just okay compared to a startup down the street. It really depends on your alternatives and priorities.
Work-life at Oracle is typically more structured – not crazy startup hours. They do have some return-to-office push (Oracle is a bit more old-school in wanting people on-site in their hubs like Austin or Redwood Shores), so be ready for that if you join. Culturally, it’s a mix of long-timers who know the ins and outs of Oracle’s legacy, and newer folks driving cloud innovation. As a data engineer, you may learn a ton from the database veterans while also contributing fresh ideas on cloud data pipelines.
Databricks – The Data Unicorn on a Hiring Spree
Rounding out our list is Databricks, one of the hottest data-focused companies out there. Databricks is not as gigantic (in employee count) as the Oracles and Amazons, but it’s growing extremely fast and hiring a lot of data engineers. Known for its Unified Data Analytics Platform (and as the original creators of Apache Spark), Databricks has become a go-to solution for big data and machine learning in the cloud. With their recent funding rounds and looming IPO rumors, they’re investing heavily in talent.
Opportunities at Databricks: If you join Databricks as a data engineer, you might actually be building the product that other data engineers use. How meta is that? Roles include developing the Databricks Lakehouse platform – improving how it handles data ingestion, streaming, or ML model serving. They also hire field engineers and solutions architects who are data engineering experts, helping customers (Fortune 500s) implement Databricks successfully. So whether you want to code the next gen of a data platform or work closely with clients on data projects, Databricks has roles for you.
Databricks operates at the cutting edge of tech. They’re into lakehouse architecture, combining the best of data lakes and warehouses. They’re also integrating a lot of AI features (like facilitating Gen AI on your data in Databricks). Working here means you’ll likely use Spark (of course), Delta Lake, and plenty of open-source tools, plus you’ll be cloud-agnostic (Databricks runs on AWS, Azure, GCP). It’s a playground for trying new approaches to data problems. They tend to look for engineers who are very strong in distributed systems and performance tuning, since the product deals with heavy workloads.
Hiring trends: Databricks is expanding globally, but being a U.S.-based company (HQ in San Francisco), a lot of their engineering hires are stateside. They have also expanded in Seattle and have new offices in tech hubs like New York and Dallas. Being a hyper-growth company, the vibe is still somewhat startupy – which means if you get in now, there’s potential for a big career trajectory and possibly lucrative stock options (if/when they IPO).
Compensation at Databricks: Databricks knows they’re competing with the big boys for talent, so they often pay very well. For example, a software engineer at Databricks straight out of school can make $180k+ total. For data engineering roles, you’re likely looking at total compensation comparable to top tech firms. Mid-level might be around $200k–$250k total, and senior roles even higher. They often include stock options (pre-IPO stock), which could be a windfall if the company continues to soar. While options carry risk, Databricks is valued highly and seems on track for more growth, so many candidates value the equity upside.
One thing to note: as a private company, equity’s value isn’t as immediately clear as public stock, but it’s something you can discuss during negotiation (evaluate how many shares, percent of company, etc.). Also, Databricks has been known to adjust comp fairly often – if the market moves, they try to keep up to retain talent. So you might see nice refreshers or raises as they grow.
If you thrive in a fast-paced environment and love working purely on data tech all day, Databricks is a dream employer. It’s like being at the forefront of where data engineering is headed. And when that IPO bell rings (likely soon), early employees might find themselves rewarded.
(Honorable mentions: other companies vigorously hiring data engineers include Snowflake (another data platform star, similar story to Databricks), consulting firms like Deloitte and EY (lots of data analytics implementation projects for clients), and finance leaders like JPMorgan and Capital One (who invest heavily in data infrastructure for fintech). While we can’t cover all of them in detail, these are also fertile grounds for data engineering careers. The common theme across all industries – every company is becoming a data company, so opportunities are everywhere!)
Data Engineer Salaries and Negotiation in 2025
By now, you might be wondering, “So what’s the bottom line on salary? How much can I make as a data engineer at these top companies?” The answer: quite a lot, especially if you play your cards right. Let’s break down the compensation picture and how to approach negotiation.
Salary ranges by experience level: As a data engineer in the U.S., your experience level is a major factor in salary. Here’s a rough breakdown in Fall 2025:
- Entry-Level (0–2 years experience): Expect offers roughly in the $90,000 to $120,000 base salary range at many large companies. Total compensation (with any bonus/stock) might land around $100k–$140k. Tech giants will be at the higher end or above – for example, a new grad at Google or Meta can easily clear $120k base plus stock. Meanwhile, a smaller company or less tech-centric firm might start closer to $90k–$100k. The good news is that even entry-level roles are often six-figure total now, reflecting the demand for fresh talent with modern skills.
- Mid-Level (3–5 years experience): This is where salaries really jump. Base salaries in the $130,000 to $160,000 range are common in big tech for mid-level data engineers (like Amazon L5, Google L4/L5). Adding bonus and stock, total comp often hits $150k–$220k. For instance, as we discussed, Amazon might pay around $150k base for an experienced DE and stock, bringing it to $220k total. Oracle or IBM might be a bit lower base (maybe $120k–$130k) and total around $150k, but that’s often offset by lower cost locales or different perks. Mid-level folks also start getting things like 401k matches, ESPP (stock purchase plans), and other benefits that effectively increase their package value.
- Senior (5+ years, lead roles): Senior and lead data engineers (think Google L6, Meta E5/E6, Amazon L6/L7, etc.), see base salaries from roughly $160,000 up to $200,000+. Total compensation can be anywhere from $250k to $400k, depending onthe company and performance. At a place like Google, a senior may have $170k base, 20% bonus, and $100k+ per year in stock – totaling around $300k. Meta often goes higher with stock, making senior total comps $350k or more. Even at “lower paying” companies, senior data engineers often cross $180k total. And if you move into management or director levels, you’re looking at even higher ranges (but that’s another story). The key point: experienced data engineers are highly valued and compensated accordingly.
Components of a compensation package: Let’s quickly define those components we keep mentioning:
- Base Salary: This is your guaranteed annual pay (before tax). It’s what most people quote when discussing salary.
- Bonus: Many companies give annual performance bonuses, typically a percentage of base. e.g., 10-20% depending on your performance and company health.
- Stock Options / RSUs: Top companies love to give equity. An RSU (restricted stock unit) grant means you’ll receive company stock over a vesting schedule (often 4 years). At public companies, this is like extra income if the stock price holds or grows. At private ones (like Databricks), it’s options that could convert to stock at IPO.
- Signing Bonus: A one-time bonus for joining. Amazon is famous for this (they’ll spread it over Year 1 and 2 paychecks). Signing bonuses can be significant ($10k, $50k, even $100k+ for very senior hires) – they help bridge any gap if stock is vesting later or if you’re leaving unvested stock at a previous job.
- Other perks: relocation assistance, 401k match (free money towards retirement), commuter benefits, etc. These aren’t “salary” but they add value.
When comparing offers, always look at total compensation and also the long-term potential. A startup’s $130k base + big equity might or might not beat a big company’s $150k base + smaller stock now – depends on that equity’s future. Know your risk tolerance.
Negotiation tips for data engineers: Now, how do you ensure you get the best offer? A few friendly tips from someone who’s been on both sides:
- Do your research: Use resources like Levels.fyi, Glassdoor, and speaking with recruiters to gauge the range for the role and level. If you know Google typically pays X for your level, and your offer is below that, you have data to negotiate.
- Leverage multiple offers: This is the classic way – if you have two offers, (politely) let Company A know that your market value as evidenced by Company B’s offer is higher, and ask if they can reconsider. Be tactful; you don’t need to name who or give exact figures initially, just indicating you have other opportunities can prompt a better offer. If they ask for details, you can choose to share ranges or specifics – sometimes an offer letter from one will get the other to match or beat it.
- Negotiate the whole package: Maybe a company can’t budge on base salary due to internal pay bands, but they might increase your signing bonus or stock grant. For example, if you’re happy with the base but not the equity, say so – “I’m very excited about the role, but considering the overall package, is there room to improve the equity portion? Given current market rates for data engineers with my experience, I was expecting something closer to XYZ.” They often will come back with more stock or a hiring bonus if the base is maxed out.
- Highlight your value: During negotiation (often with a recruiter), reiterate what you bring: “I have offers in hand and I’m ready to commit to you because I love the team and product. If we can get the compensation in line with my other options/the market, I’m eager to sign.” Show enthusiasm, but also that you know your worth.
- Be mindful of timing: Hiring budgets late in the year vs early, company performance, etc., can affect flexibility. Fall 2025 is actually a good time because many companies are planning next year’s budget now and are eager to lock in hires to kick off projects in the new year. Use that – sometimes hiring teams want to fill roles before holidays, making them more willing to negotiate swiftly.
One more thing: benefits and work-life might matter to you as much as money. Companies like Google and Microsoft offer things like sabbatical programs, great parental leave, etc. If those are important, factor them in. It’s okay to ask about those during the final stages and even negotiate aspects (e.g., sometimes you can negotiate additional PTO or remote days – not commonly, but if you need it, ask).
The bottom line on salary is that data engineering is a well-compensated field, and top companies are willing to invest to get talent on board. As long as you’ve built those in-demand skills and can demonstrate your impact, you have the upper hand to command a strong salary in this market. So set your salary expectations confidently but realistically based on data, and don’t shy away from negotiation – it’s an expected part of the process.
Skills and Experience: What Top Employers Want
Landing a job at one of these top companies isn’t just about luck – it’s about aligning your skills with what they need. So, what exactly are these firms looking for in a data engineer in 2025?
Core technical skills: Across the board, certain data engineering skills are virtually required:
- Advanced SQL: No surprise here – writing efficient SQL queries and working with relational databases (MySQL, PostgreSQL, Oracle, etc.) is fundamental. Employers expect you to handle complex joins and huge tables with ease.
- Programming (Python/Scala/Java): Python is the go-to language for a lot of data pipeline code (scripting, Airflow DAGs, etc.), so being fluent in it is key. Scala/Java come into play especially with big data frameworks like Spark, or if you’re interacting with compiled code systems. Essentially, you should be an engineer who happens to focus on data – solid coding practices, version control, and debugging skills are needed.
- Big Data Frameworks: Apache Spark is practically a must-know in big companies. Hadoop (though less hip these days) is still out there in legacy systems. Kafka or other streaming platforms (Pulsar, Kinesis) are frequently mentioned in job posts, as the shift to streaming data pipelines grows. If you can build streaming solutions that handle millions of events, you’re golden.
- Cloud Platforms: AWS, GCP, or Azure – most companies want experience in at least one. Knowing how to use cloud data services (e.g., AWS Redshift, Google BigQuery, Azure Data Factory) is extremely valuable. The more cloud-savvy you are, the quicker you can contribute to a modern data stack. Terraform or other infrastructure-as-code knowledge is a bonus as teams automate their data infra provisioning.
- ETL/ELT and Data Warehousing: Skills in tools like Apache Airflow (for scheduling pipelines) and experience designing data warehouses (star schema, fact, and dimension tables) are important. Companies want data engineers who not only move data around but also organize it for analytics. If you’ve built data models or set up a Snowflake/Redshift warehouse with good schema design, highlight that.
- Data Quality and Testing: This sometimes gets overlooked, but top employers care about data reliability. Knowing how to implement data validation, write unit/integration tests for pipelines, and monitor data quality (with tools or custom checks) will set you apart. No one wants a data engineer who just dumps data without ensuring it’s correct and usable!
Soft skills and approach: In 2025, companies want data engineers who can collaborate and adapt. That means:
- Communication: You’ll often work with data scientists, product managers, or non-tech stakeholders. Being able to explain what data you have, how it’s structured, or why a pipeline failed (in plain language) is huge. If you can turn business requirements into a data pipeline design and articulate that, you’re very valuable.
- Problem-solving mindset: Things break, requirements change. Show that you are the type to proactively fix issues and optimize processes. Maybe you implemented a self-healing mechanism in a pipeline, or you refactored a clunky ETL job to run 10x faster – those are gold star stories to share.
- Continuous learning: The data field evolves quickly. Employers love to see that you are keeping up – whether through certifications (e.g., AWS Certified Data Analytics, Google Professional Data Engineer cert), courses, or personal projects. It signals that you’ll bring new knowledge into the team, not just what you used in the past. If you’ve dabbled in trending areas like real-time analytics with Spark Structured Streaming or working with datasets for machine learning, mention it.
Experience level and scope: As noted earlier, many openings are leaning towards senior. Companies might list “5+ years experience” for a reason: they need people who can design systems, not just follow instructions. This doesn’t mean newcomers can’t get jobs – they absolutely do – but be aware of what level you’re aiming for. If you’re junior, emphasize your internships, projects, or any unique experience (maybe you built a data pipeline in a hackathon, or contributed to an open-source data tool). If you’re mid-level, highlight leadership experiences like owning a data mart or mentoring new hires. And if you’re senior, you should demonstrate architecture-level thinking (designing data platforms, making build vs buy decisions, improving team processes, etc.).
One emerging expectation is knowledge of AI/ML workflows. You don’t have to be a data scientist, but if you understand how machine learning models are trained and deployed, you can better build the data pipelines for them. Many job postings mention familiarity with ML pipelines or data science collaboration. For example, a data engineer might be tasked with creating features and feeding them into an ML training pipeline, then scheduling that retraining. If you have that experience or at least know the concepts (feature stores, model serving), it’s a plus at AI-forward companies (which most are becoming).
Make your skills match the job market: It’s worth doing a gap analysis on yourself. Check a few postings from your dream companies and see which skills you’re missing. Then you can proactively learn those. Need to brush up on Kafka or Terraform? Take a course or build a small project using them. Lacking experience with a certain cloud platform? There are plenty of free tiers and tutorials to play with. The effort you put in now can pay off big time when interviewing. To dive deeper into aligning your skill set with what employers need (especially with the rise of AI integration), consider this resource: CTA 1 – Make your skills match the Job Market. It guides you through picking up AI-ready data engineering skills that can significantly boost your hireability.
Networking and showcasing: Skills are one thing, but showing them off is another. Many data engineers build a GitHub portfolio or write about their projects on a blog or LinkedIn. This can impress recruiters and hiring managers. Imagine being able to point to a data pipeline project in your GitHub during an interview – it shows initiative and concrete ability. Also, don’t underestimate networking: attend those big data meetups or join online communities (even Reddit’s r/dataengineering or Slack groups). Sometimes job leads or referrals come from those channels, and having a personal connection can get your resume noticed faster at these top companies.
In summary, to snag a role at a top company, aim to be a well-rounded data engineer: strong in technical chops, aware of the latest tools, and able to work well with others. The field is competitive, but with the high demand, there’s room for those who prepare and present themselves effectively as the solution to a company’s data challenges.
Final Thoughts: Your Path to Landing a Top Data Engineering Role
Reading all this, you might feel both excited and a bit daunted. The opportunities are plentiful – companies are practically rolling out the red carpet for skilled data engineers – but you might also be thinking, “Do I measure up? How do I break in or move up to one of these dream companies?” The answer is step by step.
First, remember that every expert was once a beginner. The key is to keep learning and building experience, whether in your current job or through independent projects. If you’re early in your career, you might start at a smaller company or a less glamorous role to get hands-on with data pipelines. That experience is valuable and transferable. Many folks jump to big companies after a couple of years at a startup or a midsize firm, where they got to wear many hats. What matters is accumulating those real-world stories of problems solved and systems built.
Second, stay adaptable and proactive. The data landscape will continue to change – new tools, new best practices, maybe even new job titles (today it’s “Analytics Engineer” or “Data Ops Engineer” in some places). Embrace it. The fact that you’re reading this shows you care about staying informed. Keep that habit. Be the one on your team who introduces a cool new library or who volunteers to tackle the gnarly data problem no one else wants. That attitude will make you stand out.
As you aim for the top, also consider mapping out where you want to go. Perhaps you have a target: “In two years, I want to be at <DreamCompany> working on streaming systems.” Great – work backwards from that goal. Figure out what experience or connections you might need. Sometimes, you might need an intermediate step (maybe joining a company that often feeds talent into your dream company – tech is a small world, and certain company hops are common).
It’s also wise to think about the bigger career picture in data engineering. Do you want to remain hands-on technical and become a principal engineer? Or move into leadership/management eventually? Different companies offer different growth paths. For instance, Google and Meta have dual career ladders (you can go very senior as an individual contributor or become an engineering manager). Knowing what you want can help you choose roles that align with that path. For more on how a data engineering career can progress and what roles you might step through on the way to the top, check out next:
It’s a guide that can help you visualize the journey from entry-level to lead positions, and ensure you’re gaining the right experiences at each stage.
