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Data Engineering in Big Tech

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This lesson contains an analysis of job descriptions for Data Engineer roles across FAANG companies. Understanding the specific skills, technologies, and qualifications these companies prioritize will help you tailor your preparation and focus on areas that matter most to top tech employers. By aligning your knowledge and experience with what leading companies are actively seeking, you’ll be better positioned to stand out in the interview process.

Job description summary

The table below summarizes key responsibilities and qualifications found in job descriptions for Data Engineer roles at FAANG companies.

Key responsibilities:

  • Build and maintain large-scale data solutions and pipelines.
  • Ensure data quality, security, and compliance.
  • Automate and optimize data processing tasks.
  • Collaborate with cross-functional teams on data needs.
  • Educate users on data product access and usage.

Minimum qualifications:

  • Proficiency in programming languages (e.g. Python, SQL, Java, Scala)
  • Skills in data modeling, warehousing, building ETL pipelines
  • Effective communication, ability to work cross-functionally, self-driven & analytical mindset

Preferred qualifications:

  • Familiarity with Big Data technologies and experience working with large-scale data (often terabyte to petabyte range)
  • Experience with cloud technologies (e.g. AWS, Google Cloud, etc.)
  • Specialized skills based on role (e.g. workflow management engines, data privacy, ML platforms, data visualization, specific software tools)

Technical skills overview

The main technical skill categories required for most DE jobs at FAANG companies include:

  1. Programming: foundation of data operations
  2. Data Modeling & ETL: manage and transform data into usable formats
  3. Big Data Technologies: process batch and streaming data at massive scale
  4. Cloud Data Infrastructure: manage data infrastructure on the cloud

Data Engineering in Big Tech

Some additional skills may be required or preferred depending on the role. For example:

  • Workflow management engines (e.g, Airflow, Luigi, Prefect, Dagster, etc.)
  • Machine Learning platforms
  • Data privacy
  • Data visualization
  • Specific software tools

Company-specific themes

Different data engineering skills required across companies often relate to specific products or services they offer, specific technologies and tools they use, or the maturity of their data infrastructure.

Meta

  • Big data technologies
  • Workflow management engines

Amazon

  • AWS technologies
  • Modern scripting/programming language(s)

Apple

  • Machine learning platforms for AI/ML roles

Netflix

  • Batch/streaming pipelines with distributed processing frameworks

Google

  • Client-side web technologies for certain roles
  • Big data principles and techniques
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