Senior Data Quality Analyst

Komodo Health • New York, NY

9h • Hybrid

About The Position

Komodo Health is seeking a Senior Data Quality Analyst to ensure the accuracy and reliability of their Healthcare Map and AI-driven products. This role is crucial for maintaining data quality as the company scales. The analyst will act as an independent voice, verifying that data outputs are not only technically sound but also analytically correct and valuable to customers. The position involves validating data outputs, investigating bugs, and preparing for weekly publication reviews, contributing to the formalization of quality ownership within the Data Product organization.

Requirements

• 6+ years of experience in data quality, data analysis, or analytics engineering, preferably in healthcare, life sciences, or another domain with complex, multi-source data.
• Strong SQL skills for large-scale analysis (joins, window functions, aggregations, tracing data lineage). Snowflake preferred.
• Proven ability to work through ambiguous issues from signal to root cause.
• Experience designing or executing pre/post release testing, including defining attributes, tolerances, and escalation criteria.
• Familiarity with claims data (medical, pharmacy, enrollment) and common quality patterns and failure modes.
• Ability to leverage AI tools (Gemini, Claude, Cursor, etc.) to enhance personal productivity, streamline workflows, content and visualization creation.

Nice To Haves

• Python proficiency for analysis, validation, and light automation.

Responsibilities

• Run pre/post-release comparisons across key attributes to ensure changes meet data quality standards.
• Investigate issues from customer complaints and monitoring, document findings, and partner with engineering on root cause and resolution.
• Assemble the execution summary, test coverage audit, and issue disposition list to support release recommendations.
• Design and run pre/post-release comparisons across key attributes (patient counts, claim volumes, fill rates, deduplication, payer attribution, provider coverage).
• Surface and document issues missed by automated tests, such as demographic shifts, volume changes, or rule edge cases.
• Assess what changed, customer impact, and recommend action (approve, conditional approve, hold, or escalate) for each issue.
• Track what was tested, what passed, and accepted risks for each release, creating an auditable quality trail.
• Review and prioritize DPQ Jira issues, distinguishing data output problems, pipeline failures, and cases needing joint investigation.
• Query Snowflake to trace anomalies to source, validate against expectations, and rule out alternatives.
• Produce clear reports outlining the issue, evidence, likely cause, and next steps for both technical and non-technical audiences.
• Partner with Data Engineering and Architects to drive resolution and verify fixes address the root issue.
• Compile a weekly record of data pipeline execution status, anomalies, and comparison against expected behavior.
• Document which quality checks were expected and executed for each pipeline, surfacing any gaps.
• Aggregate all quality issues raised during the week into a single structured view with status, severity, and recommended disposition.
• Prepare the DPQ release recommendation document in advance of the Monday meeting.

Benefits

• Comprehensive health, dental, and vision insurance
• Flexible time off and holidays
• 401(k) with company match
• Disability insurance
• Life insurance
• Leaves of absence in accordance with applicable state and local laws and regulations and company policy.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 251-500 employees