Top Healthtech Data & Analytics Jobs in Seattle, WA

Perform program and outcomes analyses for healthcare informatics using statistical and analytical tools. Extract, clean, aggregate, and visualize large datasets; develop dashboards, reports, and recommendations. Apply advanced analytics and policy knowledge to evaluate programs, guide decisions, and present findings to stakeholders. Collaborate cross-functionally and support testing, implementation, and training.

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Lead enterprise data architecture for Privia Health’s Snowflake platform, defining scalable modeling, integration, governance, security, and optimization standards. Design Medallion Architecture layers and healthcare data schemas spanning EMRs, claims, eligibility, and value-based care data. Establish dbt, Python, testing, orchestration, and CI/CD best practices; enforce HIPAA, PHI/PII masking, access controls, lineage, and cataloging. Provide technical leadership, design reviews, mentorship, and cross-functional guidance to engineering, analytics, clinical, and product stakeholders.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Own healthcare analytics data methodology, including episode grouping, provider attribution, cost standardization, quality measures, reference data, pipelines, QA, and production delivery. Translate feature requests into specifications and validated analytics. Reconcile results, establish recurring data-quality coverage, investigate failures, and collaborate with engineering, data science, product, clinical informatics, and customer teams. Advanced SQL and healthcare claims expertise are central to ensuring accurate, defensible customer metrics.

Analyze large datasets using SQL and Python, build dashboards and recurring reports, identify trends and data-quality issues, and translate ambiguous business questions into actionable insights. The role partners with product, customer success, and engineering teams, documents metrics and methodologies, and presents findings to technical and non-technical stakeholders. Healthcare data, supply chain analytics, modern analytics engineering, and BI visualization experience are advantageous.

Leads healthcare data architecture, modeling, normalization, transformation, and integration across FHIR, OMOP, STDM, HL7, and related standards. Oversees data pipelines, terminology mapping, deduplication, quality initiatives, interoperability strategies, and technical documentation. Collaborates across technical and non-technical teams to resolve complex data issues and explore AI and machine learning applications. Requires deep expertise in clinical informatics, healthcare data standards, SQL, Python, R, dbt, ETL, cloud platforms, and version control.

Leads Point C’s enterprise analytics strategy, data platforms, governance, reporting, and insights delivery. Builds trusted analytics for brokers, employers, sales, operations, finance, and clinical teams; oversees data integration, quality, standardization, and acquisition-related analytics. Develops a scalable agile analytics organization and aligns roadmaps, metrics, and delivery standards with business growth and executive decision-making.

Own data partner specification negotiations, data mapping, Ascend platform configuration, and internal QA across multiple implementations. Review file specifications, normalize schemas, validate test files, document data dictionaries, and route non-standard requests to Product or Customer Engineering. Coordinate with delivery and engagement teams on UAT, defects, readiness, and capacity constraints. Develop repeatable QA frameworks, implementation playbooks, procedures, and training materials while ensuring configurations meet quality standards before go-live.