Senior Analytics Engineer

Clarify Health

2d•$142,000 - $167,000•Remote

About The Position

Clarify is an outcomes company that partners with health systems to address complex analytical challenges. They provide a comprehensive picture of care economics in healthcare, focusing on delivering outcomes rather than just data. The Senior Analytics Engineer will be responsible for the analytics behind the customer-facing metrics, advancing the technology that groups healthcare claims into units of analysis and transforms them into product metrics. This is a hands-on role for an analyst with healthcare data expertise, strong SQL skills, and a commitment to data accuracy.

Requirements

Bachelor's degree in a quantitative, technical, or health-related field, or equivalent practical experience
Five or more years in data analysis, healthcare analytics, or a comparable data-heavy role
Advanced SQL: ability to query large datasets without help, build metrics from raw source records, define grain, and defend values
Experience in the AWS ecosystem
Experience with industry-leading AI models, especially for code development
Working knowledge of healthcare data (claims, provider, network data) and the context around it
Experience with data in a production setting
Clear writing skills for specifications, acceptance criteria, and decision records

Nice To Haves

Depth in a healthcare domain such as episode grouping, risk adjustment, quality measurement, or provider and network reference data, including CMS or NCQA measure specifications, clinical value sets, or standards such as HL7, FHIR, and X12
Experience with BI and dashboarding tools, and explaining analysis to non-data audiences
Experience setting up recurring data quality checks
Python/Pyspark experience

Responsibilities

Own the data methodology, carrying derived data points from development through QA into production.
Write the data methodology and logic customers see, including episode triggers, provider attribution, cost standardization, and quality measure calculations.
Manage and build upon Clarify’s reference data library, including physician/facility mappings, codesets, and geographical information.
Validate numbers using reconciliation SQL, version-over-version comparisons, and impact analysis.
Turn one-off QA into standing coverage with data tests, comparisons against a known-good baseline, and completeness thresholds.
Run data refresh and delivery cadence for customer environments, troubleshooting test failures, and handing off validated groupings.
Scope feature requests from product, clinical informatics, and customer teams, writing specifications and acceptance criteria.
Settle methodology that crosses teams, balancing data quality, run time, and scope.
Hold AI-assisted tooling to the same standard as own work, checking generated output against source data.