Analytics Engineer 5 - Identity & Signal

Netflix•,

11d•$330,000 - $566,000

About The Position

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. In 2022 we launched a new lower-priced, ad-supported tier, and we are building an in-house, world-class ad-tech ecosystem to give our members more choice and to offer advertisers a premium, better-than-linear-TV experience. We are looking for the founding members of this new business area for Netflix. The Identity & Signals team builds the identity foundation of the Netflix ads business — the graph and matching systems that let us recognize the same person or household across signals, devices, and partner data sources, and that determine which signals we can responsibly and accurately use for targeting, measurement, and reporting. We build and maintain the identity graph, run and evaluate entity-matching and resolution pipelines, and manage relationships with identity providers so that advertisers and internal teams can trust the signals underneath every campaign. This is a foundational role on a small, high-leverage team. You will build the analytics and data infrastructure that powers identity matching and graph construction — designing the tables, pipelines, and metrics that let the team and its stakeholders understand match quality, coverage, and drift over time. You'll work end-to-end: from raw signal ingestion, through matching logic and graph construction, to the dashboards and data products that make identity health visible and actionable across the company.

Requirements

5+ years of experience as an analytics engineer, data engineer, or similar role working with large-scale data.
Experience working with graph data structures and graph-based data models (e.g., property graphs, graph databases, or graph analytics on top of relational/columnar stores).
Familiarity with entity matching / identity resolution concepts — deterministic and probabilistic matching, fuzzy matching, blocking/candidate generation, and match quality evaluation.
Knowledge of, or direct experience working with, identity providers such as Experian, TransUnion, LiveRamp, or similar data/identity partners.
Strong SQL and Python skills, with experience building and maintaining production data pipelines.
Experience designing data models, metrics, and dashboards that make complex technical systems understandable to non-technical stakeholders.
Demonstrated ability to collaborate closely across data engineering, software engineering, data science, and product teams.
Ability to work independently, drive your own projects, and make compelling cases for prioritization.
Embodies the Netflix values while bringing a new perspective to continue improving our culture.

Nice To Haves

Experience with ad-tech identity concepts — cross-device identity, household graphs, cookie/ID deprecation, or media measurement.
Experience with graph databases or graph-processing frameworks (e.g., Neo4j, Neptune, GraphFrames, or similar).
Familiarity with our ML/data stack (Metaflow) or comparable large-scale data tooling.
Experience working with privacy-sensitive data and an understanding of relevant data governance and privacy considerations for identity data.
Experience applying GenAI to boost developer/analytics productivity.

Responsibilities

Design, build, and maintain the data models and pipelines that support identity graph construction, entity matching, and identity resolution across first-party and third-party signals.
Partner with data engineering and software engineering to productionize matching logic, ensuring pipelines are scalable, well-tested, and monitored for data quality and drift.
Build metrics, dashboards, and analytics that quantify match rate, match quality, coverage, and graph stability, and that surface anomalies before they affect downstream campaigns.
Work closely with data science to evaluate and improve matching algorithms, providing the clean, well-documented data and features needed to test and iterate on models.
Manage and evaluate the analytics relationship with external identity providers (e.g., Experian, TransUnion, LiveRamp), including onboarding new sources, validating match performance, and monitoring ongoing data quality.
Translate identity graph and matching concepts — nodes, edges, match confidence, deterministic vs. probabilistic matching — into clear, actionable insights for product, sales, and other non-technical stakeholders.
Collaborate with product and cross-functional partners to define identity requirements, evaluate trade-offs (accuracy vs. coverage vs. privacy), and prioritize roadmap work.
Communicate technical decisions, trade-offs, and results clearly to both technical and non-technical audiences at all levels of the company.