Top Healthtech Data & Analytics Jobs

Develop and optimize SQL-based data solutions and ETL/ELT pipelines using DBeaver. Troubleshoot data quality and performance, validate data prior to production, document transformations, and collaborate with application owners and business stakeholders. Support testing, deployments, and knowledge transfer in a healthcare data environment.

Lead and own the population health analytics pipeline: intake and reconciliation of claims, eligibility, and MAO-004 data; identify and reconcile care gaps and closures; deliver practice-level performance analysis and billing reconciliations; build funnel instrumentation; document, standardize, and automate pipelines with Product and Engineering. Ensure billing-grade accuracy and support program measurement aligned to CMS reconciliation windows.

Design, build, and maintain scalable AWS-based data architecture and pipelines. Manage Redshift models and schemas, ensure data quality and observability, automate ETL/ELT using Glue, Lambda, dbt and other tools, collaborate with Data Science and Analytics, document pipelines, and mentor peers to support reliable self-service analytics.

Curate, maintain, and analyze workforce data; build and automate Power BI dashboards; deliver ad-hoc reports and insights; ensure data integrity and cleanliness; partner with stakeholders and leadership to solve data gaps and coach stakeholders on using data to inform decisions.

Lead and mentor an analytics team to design and implement data-driven solutions, align analytics with strategy, manage Agile workflows, build stakeholder relationships, and monitor KPIs to improve healthcare delivery and outcomes.

Perform statistical and data analyses supporting reentry research; design studies, manage and validate datasets, create visualizations and reports, communicate findings to stakeholders, collaborate on grants and publications, and maintain data security and documentation.

As a Clinical Research Associate, you will liaise with investigational sites, ensuring compliance, monitoring studies, maintaining documentation, and supporting recruitment efforts in oncology trials.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

Manage data transfers and internal reporting for clinical trial data, ensuring compliance with regulations and data accuracy. Collaborate with project teams to create data management templates and agreements.

Lead end-to-end measurement, optimization, and reporting for engagement and activation programs. Design and run A/B and multivariate tests, perform funnel, cohort, and segmentation analyses, build/validate dashboards and data pipelines, ensure data quality, and translate insights into executive-facing recommendations. Partner with product, engineering, creative, and client teams to drive measurable program improvements in regulated healthcare environments.