Analytics Engineer 5 - Content Finance Dse

Netflix Netflix · Big Tech · United States · Remote · Data & Insights

This role is for an Analytics Engineer at Netflix, focusing on building data products, metrics, and insights for the Content Finance team. The role involves strategic partnership with stakeholders, owning business-critical data products end-to-end, and driving root-cause analysis. While the role leverages AI tools for development acceleration, its core function is in data engineering and analytics for financial planning and content investment strategy, not in building AI models themselves.

What you'd actually do

  1. Be a strategic partner to Content Finance stakeholders — CFP&A, content planning, production finance, and content accounting — identifying impactful analytical problems and delivering data solutions that drive better decisions
  2. Own business-critical data products end-to-end: from metric definition and data modeling to documentation, testing, and ongoing reliability
  3. Champion a "data as a product" mindset — setting the standard for robust documentation, testing, and model design that makes data easy to understand, trust, and extend
  4. Design and maintain core metrics and semantic models that power planning cycles, forecasting, and executive reporting
  5. Drive root-cause analysis on data issues and workflow gaps — asking "why" to understand the drivers and proactively improve pipelines, models, and definitions

Skills

Required

  • 8+ years of experience in analytics or data engineering
  • Expert in SQL
  • Proficient in Python for data wrangling, scripting, and pipeline work
  • Strong data modeler with experience designing scalable, future-proof models
  • Experience with workflow orchestration
  • Exceptional communication skills
  • Proven ability to drive cross-functional alignment on metric definitions and data standards
  • Comfortable operating in ambiguity
  • Self-starter who takes ownership and drives projects forward with minimal oversight

Nice to have

  • Experience working with financial datasets, FP&A workflows, or content finance systems
  • Experience with big data technologies (Spark, Iceberg) and lakehouse architectures
  • Familiarity with LookML or other semantic layer tools
  • Familiarity with DBT or other semantic modeling tools

What the JD emphasized

  • Expert in SQL
  • strong business partnership
  • product mindset
  • data as a product
  • business-critical data products
  • core metrics
  • data issues
  • workflow gaps
  • cross-functionally
  • metric definitions
  • data standards
  • operating in ambiguity
  • ownership
  • minimal oversight