Top Data & Analytics Jobs

The Technology Governance Automation Engineer enhances compliance and governance processes through automation, data analysis, and collaboration with various teams to ensure efficient and auditable operations.

Lead the development and implementation of advanced reporting and analytics tools to support clinical teams while managing a team of analysts. Collaborate cross-functionally to ensure high-quality deliverables and foster a high-performance culture.

The Director of Data Analytics will lead quantitative analysis for Medicare and Medicaid models, mentor staff, and leverage AI in healthcare data projects.

The Principal Data Engineer will design and maintain data architectures, build scalable pipelines, ensure data quality and governance, and enable AI/ML applications. Responsibilities include collaborating with various teams and optimizing data performance for enterprise applications.

The Capacity Planner analyzes and forecasts operational data, develops business solutions, and supports strategic activities, focusing on metrics in call center operations.

The Principal Data Engineer will design and implement cloud-based data architectures, manage pipelines, ensure data quality, and provide mentorship to junior engineers while leading data initiatives within the company.

The role leads a team to provide healthcare-focused insights through analytics and data science, requiring strong people management, technical oversight, and a healthcare domain understanding.

Lead data management and analytics efforts for healthcare economics: acquire, standardize, map, and validate data; support Snowflake-based infrastructure and AI enablement; perform SQL development, quantitative analyses, UAT, and act as liaison between business, IT, and project teams to document requirements and ensure data integrity.

Lead design and delivery of resilient, scalable cloud data solutions on AWS using Spark/PySpark. Drive CI/CD (GitHub Actions), IaC (Terraform), telemetry/observability, automation, testing, and SQL optimization. Partner with product and architecture, mentor engineers, collaborate across teams, and reduce operational churn through automation.

Validate ETL processes, verify source-to-target mappings, and test data transformations in the EDW. Use SQL, Python and AI tools to automate tests and ensure dashboard/report accuracy. Create test plans/cases, collaborate with data engineers and analysts, support defect resolution, and drive quality across data platforms and pipelines.