Analytics Engineer 5 - Ads Reporting & Metric Standards

Netflix is a company dedicated to entertaining the world and is seeking a Senior Analytics Engineer to own the analytics foundation for advertiser-facing reporting. This role involves ensuring metric consistency across the ad platform and contributing to the Ads Metric Catalog to enhance the reporting capabilities.

Responsibilities:

• Go deep on reporting. Serve as the dedicated Analytics Engineer for the platform's campaign reporting capabilities — default reports, new report builds (e.g., billed cost reports), partner-built reporting modules (e.g., regulatory compliance reporting for international markets), and the data models that power them. This module is expanding, and the scope will grow with it. You'll work closely with the Campaign Reporting PM on prioritization and roadmap, and with DE on schemas and data contracts
• Go wide on metric standards. Act as the steward for cross-module metric consistency in partnership with Product, DE, Finance, and other module owners. Reporting is the reference implementation — your metric definitions, source dataset choices, and calculation methodologies serve as the benchmark against which other modules align. That said, the platform is evolving, and new modules will bring new questions about how metrics should be defined. Part of the job is working through those questions with module teams as they come up — ideally early, when they're designing new metrics or data models, so the reference patterns are part of their build process rather than a retrofit
• Build and extend the Ads Metric Catalog into the ad platform. As a member of the team that owns AMC, you'll contribute directly to the semantic layer — writing metric definitions, building materialization logic, and developing governance workflows. You'll help ensure reporting is a flagship consumer of AMC-governed metrics and build adoption patterns that other modules can follow
• Partner closely with the Campaign Reporting PM. The PM defines what the product should do. You define how the metrics that power it are calculated, validated, and governed. As the platform scales for external advertisers, that partnership is where product vision meets data rigor
• Partner with Data Engineering on Data Foundations. Work with the Reporting DE lead to ensure metric standards are embedded in the ongoing backend rearchitecture — shared data models, consolidated reporting schemas. Reporting is the primary proving ground for this rearchitecture, and the patterns you establish here will shape how newer modules build their data foundations
• Float across the platform as needed. When other modules need AE support — reviewing data models against the shared schema, validating new metrics, or ensuring they follow the patterns reporting has established — you step in. As the ad platform evolves, this kind of cross-module support becomes more important, not less
• Participate in cross-functional governance. Represent the reporting and metric standards perspective on cross-team councils and platform federation processes — reviewing specs, contributing to data standards, and helping ensure metric consistency between analytical and operational surfaces alongside DE, Product, and Platform Engineering partners. As new modules come online and existing ones evolve, you'll help ensure metric definitions stay aligned — surfacing questions early and working with the relevant teams to resolve them

Requirements:

• Full-stack data fluency. SQL, Python, and experience with semantic modeling and metric computation. Exposure to columnar analytics stores (e.g., Druid) and lakehouse architectures (e.g., Iceberg) is valuable — deep expertise in at least one analytical data platform is needed for the role
• Metric governance experience. Familiarity with semantic modeling (LookML, DataJunction, or equivalent) and data quality frameworks. You understand that defining a metric across a matrixed organization is as important as writing the code to compute it
• A product-minded approach to metrics. You're comfortable with ambiguity, partner closely with PMs and DEs, and can drive consensus on metric definitions across Product, Engineering, and Analytics teams. You think like a technical lead for the semantic layer — not just building it, but shaping how others use it
• Communication and influence. The ability to work with PMs, DEs, and AEs across module teams on shared definitions and standards. You connect the dots between what the numbers say and what they should mean
• Workflow orchestration. Experience with tools like Apache Airflow or Maestro, and analytics scripting for building and maintaining data pipelines
• API fluency. Familiarity with API design, querying, and integration (e.g., GraphQL, REST) — we're building out both external and internal APIs for querying data and serving metric definitions, and this role will increasingly intersect with those systems
• Interest in AI and agentic workflows. We're investing in how AI can accelerate metric governance, data quality, and self-serve analytics. This role will intersect with that work, so curiosity about how LLMs, agents, and AI-powered tooling can improve analytics workflows is valuable
• Awareness of external data quality standards (e.g., MRC) is a plus — our reporting surfaces serve external advertisers and operate under strict data integrity expectations