Career Development

FAANG+ Data Engineer Learning roadmap for 2024

The data engineering future of FAANG+ companies in 2024 will be defined by advanced data systems orchestration, requiring mastery of a sophisticated set of technologies and methodologies. By 2024, FAANG+ companies will require data engineers to have a strong understanding of computer science principles and programming skills, as well as expertise in distributed data architectures, real-time processing frameworks, and cloud-based data solutions.

This roadmap is designed for individuals who want to excel in data-intensive environments at leading tech companies. It focuses on critical competencies such as advanced SQL querying and optimization, effective use of big data ecosystems, proficiency in cloud data services, and strategic data pipeline development.

This article prepares candidates to face current challenges and future technological advances in data engineering.

Foundational Knowledge

Proficiency in programming languages instrumental in data manipulation and processing tasks is at the core of data engineering. Python remains the language of choice due to its readability, versatility, and extensive support provided by libraries such as Pandas for data manipulation and PySpark for working with big data. Java is essential for developing high-performance back-end systems. It is commonly used for building scalable data processing applications that can handle the throughput demands of big data ecosystems.

The skill to interact with databases, including extracting, inserting, updating, and deleting data, is essential. However, top-tier companies like FAANG+ expect proficiency beyond basic CRUD operations. Engineers must master advanced SQL techniques, optimize queries for performance, and design databases that support scalability and high availability. This requires a deep understanding of complex joins, window functions, and the creation and management of indexes to optimize data retrieval operations in relational databases such as PostgreSQL and MySQL.

FAANG+ companies require a strong foundation in distributed systems principles and big data processing frameworks to manage their vast amounts of data. It is essential to understand the architecture and operation of systems like Hadoop for distributed storage and Spark for fast, in-memory data processing. Familiarity with real-time data processing platforms like Apache Kafka, which enable the streaming and processing of data at scale, is also crucial.

Familiarity with AWS, Google Cloud Platform, or Microsoft Azure services for data storage, processing, and analysis is essential. This includes services such as AWS S3 for data storage, Google BigQuery for data warehousing, and Azure HDInsight for cloud-based Hadoop and Spark services.

Note: The pathway to becoming a data engineer at a FAANG+ company in 2024 involves not just acquiring these foundational skills but also applying them to solve complex, real-world problems. 

Advanced-Data Management

Big Data Processing Frameworks

Advanced data management requires the ability to handle and process data at scale using distributed computing frameworks.

FrameworkUse CaseKey Features
Apache HadoopDistributed storage and batch processing of large data sets.DFS for storage, YARN for job scheduling, and MapReduce for processing.
Apache SparkIn-memory data processing for batch and stream processing.The software enables rapid processing and supports SQL queries, machine learning algorithms, and graph processing.

NoSQL Databases

NoSQL databases provide flexible schemas for unstructured and semi-structured data, making them a necessity for data engineers working in environments that require scalability and high performance.

Database TypeExampleCharacteristics
Key-ValueDynamoDBSimple, highly scalable, suitable for caching and session storage.
DocumentMongoDB, CouchbaseFlexible schema, JSON-based storage, suitable for content management and mobile apps.
GraphNeo4j, Amazon NeptuneRelationships are first-class citizens, suitable for social networks, fraud detection, and recommendation systems.

Data Warehousing Solutions

FAANG+ companies also use cloud-based data warehousing solutions to efficiently store, analyze, and retrieve large amounts of structured data.

RedshiftAmazonColumnar storage, data compression, and parallel execution of queries.
BigQueryGoogleServerless, highly scalable, supports SQL and ML through SQL with BigQuery ML.
SnowflakeSnowflake Inc.Unique architecture separating compute from storage, enabling on-the-fly scalability.

Real-time Data Processing

The capability to process and analyze data in real-time is increasingly becoming a staple requirement, facilitating immediate insights and actions.

Apache KafkaReal-time streaming platform.High throughput, scalable, durable, and fault-tolerant publish-subscribe messaging system.
Apache FlinkStream processing framework.Supports event time processing, exactly-once semantics, and stateful computations

Data Integration and ETL Processes

Data engineers must understand the nuanced distinction between ETL and ELT processes. ETL processes transform data before loading it into a target system, while ELT processes transform data after it has been loaded, leveraging the processing power of modern data warehouses. The move towards ELT reflects the changing data landscapes in FAANG+ companies. Cloud-based data warehouses such as Google BigQuery and Snowflake allow for dynamic transformations, accommodating the growing speed and amount of data.

Efficient data integration requires automating ETL processes to ensure smooth data flow from source to destination with necessary transformations applied. Tools like Apache Airflow and DBT have become instrumental in this regard. Apache Airflow enables scheduling and monitoring of data pipelines using directed acyclic graphs (DAGs), providing a flexible platform for defining complex dependencies and workflows. DBT specializes in transforming data within the warehouse, enabling data engineers to define transformations as code for highly reproducible and scalable processes.

Real-time data processing has become a standard expectation in data engineering roles within FAANG+ companies. Apache Kafka and Apache Flink are leading technologies in this transformation. Apache Kafka is a distributed event streaming platform that can handle trillions of events daily, enabling real-time data feeds into analytics systems and applications. Apache Flink complements Kafka by providing a stream processing framework with built-in support for event time processing, windowing, and state management. This is essential for developing complex real-time analytics applications.

Cloud Computing and DevOps for Data Engineering

Cloud Platforms for Data Engineering

The choice of a cloud platform has a significant impact on the architecture and capabilities of data engineering solutions. Each platform provides a specific set of services tailored to data storage, processing, analytics, and machine learning.

Cloud PlatformNotable ServicesData Engineering Use Cases
AWS (Amazon Web Services)S3, Redshift, EMR, Lambda, GlueData lakes, warehousing, real-time analytics pipelines
Google Cloud Platform (GCP)BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud StorageBig data analytics, serverless data processing, data lakes
Microsoft AzureAzure Data Lake Storage, Synapse Analytics, HDInsight, Azure Functions, Stream AnalyticsEnterprise data warehousing, big data processing, real-time streaming

Data Engineering Tools in the Cloud

Leveraging cloud-native tools can significantly enhance the efficiency and scalability of data engineering workflows. These tools are designed to integrate seamlessly with cloud services, providing robust solutions for data integration, ETL/ELT processes, and data pipeline orchestration.

ToolCloud IntegrationFunctionality
Apache AirflowAWS, GCP, AzureWorkflow orchestration and scheduling of data pipelines
TerraformAWS, GCP, AzureInfrastructure as Code for provisioning and managing cloud resources
Apache SparkAWS EMR, GCP Dataproc, Azure HDInsightLarge-scale data processing and analytics

DevOps Practices for Data Engineering

Incorporating DevOps practices in data engineering ensures continuous integration, delivery, and deployment (CI/CD), facilitating agile development and operational efficiency. Key practices include automated testing of data pipelines, version control of data models, and monitoring and logging of data workflows.

Continuous Integration / Continuous Deployment (CI/CD)Jenkins, GitLab CI, GitHub ActionsAutomates the testing and deployment of data pipelines, reducing manual effort and errors
Infrastructure as Code (IaC)Terraform, CloudFormationAutomates the provisioning of cloud infrastructure, ensuring consistency and scalability
Monitoring and LoggingPrometheus, Grafana, CloudWatchProvides insights into the performance and health of data pipelines and infrastructure, enabling proactive optimizations

Professionals who adapt to the synergies between Cloud Computing, DevOps, and Data Engineering will be equipped with the capabilities to efficiently architect, deploy, and maintain sophisticated data ecosystems. 

SQL FAANG Problems Course

Data Engineer Academy presents the SQL FAANG Problems Course, meticulously designed to prepare candidates for the high standards of SQL proficiency expected by top tech companies. It features a series of real-life SQL challenges that reflect the complexity of problems faced by engineers at Facebook, Amazon, Apple, Netflix, and Google.

Facebook SQL Problem Example

In this module, you’ll encounter scenarios such as analyzing user engagement with different content types on social platforms. For instance:

Write a query to identify the top user who has given the most reactions specifically to ‘Video’ content type.

This problem requires you to join the ‘posts’ table with the ‘reactions’ table, filter by ‘content_type’, group the results by ‘user_id’, and count the reactions to determine the most active user engaging with video content. The module provides step-by-step guidance on how to approach the problem, construct an efficient SQL query, and interpret the results, emphasizing the nuances of data analysis in a social media context.


Each section of the course focuses on a different FAANG company, presenting unique problems that target the specific data challenges and business contexts of each entity:

  • Facebook – analyzing social media interactions and content engagement.
  • Amazon – querying e-commerce transactional data and customer interactions.
  • Apple – managing media and device data, focusing on performance and user experience.
  • Netflix – working with large-scale streaming data, content preferences, and viewing patterns.
  • Google – handling search data, advertising metrics, and cloud-based data analytics.

By completing the SQL FAANG Problems Course, you’ll not only refine your SQL query writing skills but also gain insights into the strategic thinking behind data problem-solving at these leading companies. The course includes interactive SQL challenges, expert reviews, and hands-on projects to solidify your understanding and application of advanced SQL techniques.

Join the Data Engineer Academy courses today and gain hands-on experience solving the kind of SQL problems that define data engineering roles in these top-tier companies. Don’t miss this opportunity to elevate your career with the expertise that makes a difference.