Apply hands-on analytics engineering expertise to solve complex, fast-paced business and clinical problems using data.
Design, develop and maintain scalable, analytics ready data models and pipelines (primarily in dbt) that power clinical program reporting, metrics and insights in our Unified Data Warehouse (UDW).
Partner closely with clinical program leaders, analysts, product teams, and operations stakeholders to understand program workflows (e.g., enrollment, adherence, outcomes, follow ups) and translate them into intuitive, well-documented data models.
Build curated data marts and semantic layers that make analytics for clinical programs (such as diabetes and weight management) easy to self-serve for dashboards, ad-hoc analysis and operational reporting.
Own KPI and metric definitions end-to-end, from raw source data through production-grade models, ensuring consistency, transparency and trust in reporting.
Implement automated data validation, testing and quality checks to ensure high data reliability across clinical and operational datasets.
Continuously optimize data models and warehouse performance for both cost and speed as usage scales.
Contribute to analytics governance by establishing guardrails, documentation and best practices that improve accountability, ownership, and data literacy across teams.
Communicate insights effectively by developing clear data memos, documentation and presentations tailored to clinical and business stakeholders.
Explore and integrate AI-enabled analytics and predictive modeling use cases (e.g., adherence risk, program outcomes, operational forecasting) into the analytics stack. Experience operationalizing ML models is a plus.

Requirements

3 to 4 years working in data analytics and analytics engineering.
Expert-level proficiency in SQL and statistical programming (Python, R).
Expert-level proficiency in DBT (data build tool).
Experience in cloud platforms (preferably in AWS) and hands-on experience in cloud data warehouses (Redshift or Snowflake).
Experience in dimensional modeling and defining star/snowflake schemas to create a foundation for reporting.
Strong business acumen, preferably with exposure to clinical programs, care management, or healthcare operations.
Ability to design data models that reflect real-world clinical workflows, not just technical schemas.
Strong analytical and problem-solving skills.
Ability to understand, tackle, and solve problems from both technical and business perspectives.
Experienced with developing data tools, memos, and presentations to deliver data and insights to stakeholders.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonRDBTdimensional modelingstar schemasnowflake schemadata validationpredictive modelinganalytics engineering

Soft Skills

analytical skillsproblem-solving skillsbusiness acumencommunication skillscollaborationdocumentationdata literacyownershipaccountabilitypresentation skills