Data Analyst, Analytics & Informatics

Purpose:
UPMC Enterprises (UPMCE) is the innovation, commercialization, and venture capital arm of UPMC, a $24 billion health care provider and insurer based in Pittsburgh, PA. With a mission to create products and businesses that make life-changing medicine happen, UPMCE invests financial and intellectual capital into developing solutions that benefit the entire health care industry with two broad focus areas: Digital Solutions and Translational Sciences. We have a diverse portfolio, supporting both early and growth stage companies with the wealth of expertise from UPMC.

Please note that this position is in-office 3 days per week.

The Team
The Technology & Data Services team offers technical and business services for UPMCE portfolio companies, investments, and strategic partners creating innovative healthcare solutions to drive clinical and financial outcomes. We support all stages of a healthcare technology venture’s lifecycle with strategic, implementation, and operational services. The Data Analytics and Informatics Service within the Technology & Data Services team provides key data-driven insights for both Digital Solutions and Translational Sciences focus areas to address critical business questions supporting investment and product development life cycles.

The Role
The Intermediate Data Analyst is an independent contributor who owns moderate-complexity analytics workstreams from intake through delivery. This role partners with clinical, business, product, and technical stakeholders to clarify requirements, develop reusable queries and analytic workflows, and deliver dependable insights that support investment, product, and operational decisions. The Intermediate Data Analyst builds analytics-ready datasets, develops interactive dashboards with appropriate access controls, and contributes to data dictionaries, lineage documentation, and reproducibility practices. The role is expected to participate in requirements gathering — including customer and pre-sales activities where appropriate — automate repetitive analytic tasks, and present concise readouts that translate findings into clear, actionable recommendations for technical and non-technical audiences.

Responsibilities:

• Data Sourcing, Quality Assurance & Analytics Delivery
  • Own moderate-complexity data extracts and build reusable SQL queries, notebooks, and analytic workflows aligned to team standards.
  • Implement automated QA checks, reconcile anomalies with subject-matter experts, and surface root causes for data quality issues.
  • Contribute to data dictionaries, source-to-target mappings, and lineage documentation that support discoverability and reuse.
  • Segment populations and design comparison groups in collaboration with subject-matter experts to support sound interpretation of results.
  • Apply appropriate descriptive statistics, confidence intervals, and exploratory techniques to characterize cohorts and surface meaningful patterns.
  • Build feature datasets, usage and retention analyses, and funnels that quantify product behavior and outcomes.
  • Map data to standard healthcare vocabularies (OMOP or equivalent) where required, and validate definitions with stakeholders.
  • Parameterize notebooks, manage version control of analytic assets, and publish results to team standards for reproducibility.
• Reporting, Visualization & Stakeholder Engagement
  • Develop interactive dashboards with filters, drill-throughs, and row-level security appropriate to the audience.
  • Optimize SQL and DAX, implement usage telemetry and alerting, and version-control BI artifacts with documented data lineage.
  • Draft analysis plans, visualize impact trends, and produce concise readouts with visuals, caveats, and recommendations.
  • Participate in requirements gathering sessions — including customer pre-sales activities where appropriate — to clarify data questions and confirm definitions.
  • Develop stakeholder empathy through active listening, facilitation, and crisp visual storytelling that tailors content to the audience.
• Reproducibility & Continuous Improvement
  • Publish reproducible notebooks and pipelines with clear readmes, lineage metadata, and tagged datasets.
  • Participate actively in peer reviews and refactors, contributing to playbooks, shared libraries, and reusable analytic components.
  • Automate repetitive analytic tasks to reduce toil and increase the team’s delivery capacity.
  • Run small, well-scoped experiments to evaluate new techniques or tools, measure their impact, and share results with the team.
  • Stay current on analytics practices, tools, and healthcare data standards, and contribute to team learning through demos, brown-bags, and shared examples.