Writing from our team. The latest news, insights, and resources.
Referring a friend to something you genuinely believe in is one of the simplest yet most powerful ways to create opportunities. With that in mind, we’re excited to introduce the Data Engineer Academy Referral Program—a way to reward you for sharing the benefits of industry-leading data engineering training with the people you know. We designed...
In today’s digital world, both individuals and businesses require a powerful website. However, finding a trustworthy hosting company is an important step in creating a website. Amazon Web Services (AWS) EC2 provides a strong and scalable infrastructure for hosting websites, making it a great alternative for your hosting requirements. Step-by-step instructions for how to host...
You can move from BI analyst to data engineer by building skills in Python, data pipelines, cloud platforms, testing, and software engineering practices. Your SQL, data modeling, and business knowledge provide a useful foundation. The main upgrade is learning to build and maintain reliable systems that other teams can trust. Start by expanding the skills...
Test data quality in dbt by adding checks to models and sources, running them with dbt test, and fixing failures before unreliable data reaches users. Start with critical keys and business rules, then run those checks during development and automated builds. The practical answer to “How to Test Data Quality in dbt” begins with deciding...
Strong Snowflake SQL interview prep combines standard SQL skills with knowledge of Snowflake features, query behavior, and practical trade-offs. These Snowflake SQL interview questions with answers cover beginner to mid-level roles, plus scenarios that test experienced candidates’ judgment. You need to explain why your query works, not just produce valid syntax. Start with architecture, then...
Prepare for a data engineering interview by matching your practice to the role, reviewing SQL and Python, explaining design choices, and rehearsing project and behavioral answers. Prioritize clear reasoning over memorizing every tool. Knowing how to prepare for a data engineering interview helps you turn limited study time into focused practice. Start with the job...
Here’s how to use LinkedIn to land data engineering jobs: show proof of your skills, connect with relevant people, and search deliberately. Your profile should make your technical evidence easy to find before you contact recruiters or submit applications. Beginners can use portfolio work; career changers and working professionals can add relevant experience without overstating...
To answer behavioral interview questions as a data engineer, tell a real story using Situation, Task, Action, and Result (STAR). State what you owned, explain the decisions you made, and close with the effect on your team or its users. Interviewers assess communication, judgment, ownership, and teamwork alongside technical skill. A pipeline failure, for example,...
Data quality matters because every report, model, and decision depends on data that is accurate, complete, consistent, and timely. For data engineers, protecting that quality starts at the source and continues through ingestion, transformation, and reporting. A pipeline can finish on schedule and still deliver the wrong answer. Well-placed checks help you catch problems before...
Snowflake certifications can show employers that you understand the platform, but the right credential depends on your experience and target role. In 2026, a badge can help make your skills easier to identify, especially when you’re changing careers or moving into a Snowflake-heavy team. It can’t replace the judgment you gain by building and troubleshooting...
Most career content walks you up to senior data engineer and stops, as though the job has one ceiling. It doesn’t. Base pay past that point climbs past $220,000, and total compensation at the top of the technical track runs beyond $300,000 at large employers. But there’s a question that matters more than which branch...
The analytics engineer vs data engineer decision usually gets framed as a hierarchy, with analytics engineering as the junior version. That framing is wrong and it costs people money. Here’s the number that surprises most people: Glassdoor puts the average analytics engineer at $156,743 against $134,644 for a data engineer. On the headline figures, analytics...
The instinct after a layoff is to start applying immediately. It feels like the responsible thing to do, and it’s usually the wrong first move. The first week has deadlines attached to it severance review windows, benefits elections, unemployment filing and several of them close permanently. Applications don’t have deadlines. Spend week one on the...
Snowflake Snowpipe continuously loads new files from cloud storage into Snowflake without waiting for a scheduled batch job. It fits event data, application logs, IoT records, and near-real-time analytics where files arrive throughout the day. Snowflake Snowpipe is near real time, not true millisecond streaming, so expect freshness in minutes rather than instant row-by-row delivery....
The question underneath “am I too old to become a data engineer” is usually not about ability. It’s about whether the investment pays back before you run out of runway, and whether anyone will hire someone who looks like a beginner at 43. The honest answer has three parts. The disadvantages are real and worth...
Needing visa sponsorship is usually treated as a legal problem. For your job search, it’s a targeting problem first. Most candidates who need sponsorship run the same search as everyone else, apply broadly, and lose three months discovering that the majority of employers were never going to sponsor anyone. The information needed to avoid that...