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Data Engineer

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Onsite

Data Engineer

Amazon

Data Engineer

Skills

SQL QueriesBig DataData EngineeringPythonAmazon RedshiftData AnalysisAIAWSMarketing StrategyHadoop

22 days ago

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Important information


Salary:

$132,100 – $178,800 / year

Location:

Los Angeles, CA, USA

Work mode:

Onsite

Published on:

9 October 2026

What they need


Who you are

  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
  • 3+ years of data engineering experience
  • 3+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
  • Experience in scripting for automation (e.g. Python) and advanced SQL skills
  • Experience in building analytic or scientific data products or solutions
  • Experience building data pipelines or automated ETL processes
  • Knowledge of distributed systems as it pertains to data storage and computing
  • Experience working with data warehousing, data quality
  • Master's degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field
  • Experience using Cloud Storage and Computing technologies such as AWS Redshift, S3, Hadoop, etc
  • Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
  • 6+ years of business intelligence and analytics experience
  • Experience in digital media or marketing strategy, data analysis, analytics & visualization
  • Familiarity with data governance, lineage, and metadata management practices

What the job involves

  • We are looking for a Data Engineer to join a dedicated, LA-based Studios Analytics team embedded alongside Amazon MGM Studios leadership
  • Reporting to the Senior Manager of Studios Analytics, you will build and own the data pipelines and models that power content performance reporting and the products Studios leaders rely on daily
  • You will own foundational content performance datasets end-to-end — turning a historically fragmented, dependency-heavy data landscape into trusted, single-threaded, well-governed data products
  • This is a hands-on data engineering role at the heart of a deliberate structural change: bringing the content performance data and the products that surface it under one team, removing cross-team dependencies, and enabling faster iteration for Studios stakeholders
  • Build, optimize, and own data pipelines (ETL/ELT) and logical/physical data models for content performance datasets that serve as the source of truth for title-level launch KPIs
  • Own ongoing data quality, monitoring, and SLAs for your datasets, ensuring reporting surfaced in applications is accurate, timely, and trusted by Studios leadership
  • Take single-threaded ownership of the data behind the content performance products enabling the team to prioritize and deliver data changes independently
  • Partner with BIEs, product development, and the financial reporting team to deliver integrated title-level and portfolio-level data (including feeds into web applications and downstream reporting)
  • Consume upstream governed data (e.g., P&L from the Economics team) correctly and consistently, upholding metric governance so definitions remain consistent across Prime Video
  • Automate manual and ad-hoc data processes, build tooling for data lineage, quality, and discoverability, and reduce operational toil
  • Contribute to design reviews, data modeling decisions, and technical trade-offs, leaving data solutions better and easier to maintain than you found them
  • Enable self-service access to underlying content data for analysts and BIEs, minimizing bottlenecks
  • This role sits on a newly-expanded LA Studios Analytics team embedded with Studios but part of the broader 40+ person central Prime Video Finance Analytics organization
  • This structure is deliberate: you'll have the proximity of a small, focused team partnering directly with Studios VPs and Strategy, with the leverage of shared central infrastructure, governed data, and a dedicated AI enablement function behind you

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