Senior Data Quality Analyst

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We Breathe Life Into Data At Komodo Health, our mission is to reduce the global burden of disease. And we believe that smarter use of data is essential to this mission. That's why we built the Healthcare Map - the industry's largest, most complete, precise view of the U.S. healthcare system - by combining de-identified, real-world patient data with innovative algorithms and decades of clinical experience. The Healthcare Map serves as our foundation for a powerful suite of software applications, helping us answer healthcare's most complex questions for our partners. Across the healthcare ecosystem, we're helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease. As we pursue these goals, it remains essential to us that we stay grounded in our values: be awesome, seek growth, deliver "wow," and enjoy the ride. At Komodo, you will be joining a team of ambitious, supportive Dragons with diverse backgrounds but a shared passion to deliver on our mission to reduce the burden of disease - and enjoy the journey along the way. The Opportunity at Komodo Health Quality is core to Komodo Health's mission to reduce the global burden of disease. As we scale the Healthcare Map® and AI-driven products, the need for independent, rigorous data quality oversight is critical. We're not hiring a traditional QA or testing specialist. We're looking for an analytically rigorous, detail-oriented professional who knows pipelines can run flawlessly yet produce analytically wrong data-and that meeting specs doesn't always mean meeting customer needs. This is where the Senior Data Quality Analyst comes in. You'll be the independent voice on data outputs-asking not just "did it run?" but "does this make sense for the customer?" Grounded in how customers use healthcare data, you'll drive the analysis and quality monitoring needed to ensure it delivers real value. Role Mission & Mandate The Senior Data Quality Analyst will own three standing operational responsibilities that are critical to Komodo's weekly data delivery and major version release process: Data output validation: Run pre/post-release comparisons across key attributes to ensure changes meet data quality standards-not just engineering specs. Bug investigation support: Investigate issues from customer complaints and monitoring, document findings, and partner with engineering on root cause and resolution. Weekly publication review preparation: Assemble the execution summary, test coverage audit, and issue disposition list to support DPQ's Monday release recommendation. Why This Role, Why Now Komodo's Data Product org is evolving-formalizing quality ownership, embedding automated QA, and establishing DPQ as the independent voice for every release. You're joining at the moment that authority is being defined and put into practice. Looking back on your first 12 months at Komodo Health, you will have... I. Data Output Validation (primary focus) The core of this role is independently assessing whether Komodo's data outputs are analytically sound-ensuring the data tells the right story for customers, not just that the engineering works. Release validation: Design and run pre/post-release comparisons across key attributes (patient counts, claim volumes, fill rates, deduplication, payer attribution, provider coverage). Anomaly identification: Surface and document issues missed by automated tests-valid but suspicious patterns like demographic shifts, volume changes, or rule edge cases. Disposition recommendations: For each issue, assess what changed, customer impact, and recommend action (approve, conditional approve, hold, or escalate). Coverage documentation: Track what was tested, what passed, and accepted risks for each release-creating an auditable quality trail. II. Bug Investigation & Root Cause Analysis Data quality issues rarely surface clearly-this role requires the rigor to navigate ambiguity and the precision to communicate findings to both technical and non-technical stakeholders. Issue triage: Review and prioritize DPQ Jira issues, distinguishing data output problems (DPQ), pipeline failures (engineering), and cases needing joint investigation. Hands-on investigation: Query Snowflake to trace anomalies to source, validate against expectations, and rule out alternatives. Findings documentation: Produce clear reports outlining the issue, evidence, likely cause, and next steps for both technical and non-technical audiences. Resolution coordination: Partner with Data Engineering and Architects to drive resolution and verify fixes address the root issue. III. Weekly Publication Review Preparation Each week, Komodo publishes updated data. DPQ owns the release recommendation at the Monday meeting, and this role assembles the inputs that inform that decision. Pipeline execution summary: Compile a weekly record of wh

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