Pay
$122K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 321 postings in the last year. Stated ranges, not salaries paid.
Remote, OR, US
Live, first seen by us 14 days ago* 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. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $330,000.00 - $566,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.Read the full posting
Listed location: Remote, OR,US
Work setup in the employer’s posting: TELECOMMUTEFrom the employer’s posting
Employment type in the employer’s posting: FULL_TIMEFrom the employer’s posting
Design and maintain data models and pipelines for identity graph construction and entity matching
Build metrics and dashboards for match rate, match quality, coverage and graph stability
Manage analytics relationships with external identity providers, including onboarding and data quality checks
Work with data science to evaluate and improve matching algorithms
Turn identity graph and matching concepts into insights for non-technical stakeholders
Design, build, and maintain the data models and pipelines that support identity graph construction, entity matching, and identity resolution
Build metrics, dashboards, and analytics that quantify match rate, match quality, coverage, and graph stability
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.
Work closely with data science to evaluate and improve matching algorithms
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.
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$122K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 321 postings in the last year. Stated ranges, not salaries paid.
Day 14
Similar jobs usually come down between day 6 and day 47.
From 1,409 similar postings that came down. Coming down is not always a hire.
28 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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We Are Looking For * 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 Have
Nice To Have * 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. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $330,000.00 - $566,000.00. This compensation range will vary based on location.
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We file this job under Analytics Engineer, by the work the posting describes.
Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.
Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.
A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.
The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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Remote, OR, US
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