Lead Data Engineer

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Risk Adjustment and Stars Analytics (RASA) delivers integrated solutions that leverage high-quality data, data-driven insights, and technology to create differentiated member and business partner experiences that drive measurable value (clinical, revenue, and administrative) and mitigate risk. RASA is part of the Quality Cost Strategy Analytics (QCSA) organization, which operates with a spirit of shared success to enable the enterprise to meet value targets in Trend, Stars, and Risk Adjustment.

This role will be closely aligned with the Part D and PDP teams under the Provider Programs organization within Risk Adjustment. The Lead Data Engineer handles work assignments involving complex issues where the analysis of situations or data requires an in-depth evaluation of variable factors.

The Lead Data Engineer in RASA (Risk Adjustment and Stars Analytics) will be a talented associate who builds strong relationships with customers to understand business needs and expectations. This individual will improve data access patterns, strengthen pipeline governance, and help establish a maintainable approach to Databricks pipelines and workflows as the Part D/PDP data universe matures its analytics capabilities. The role is accountable for architecting, modernizing, and optimizing the Part D and PDP data platform to improve reliability, reduce technical debt, strengthen observability/fault tolerance, and increase engineering efficiency in a complex, changing environment.

This is a lead-level, individual contributor role requiring high autonomy and the ability to facilitate work across multiple teams, including IT. The needs of our business segment change frequently, so the ideal candidate will leverage subject matter experts and other resources, clearly communicate across technical and business teams, and resolve ambiguity to solve business problems with curiosity. A successful candidate will be able to clearly explain the details of their work to both customers and the team.

Candidates must be passionate about contributing to an organization focused on continuously improving consumer experiences. The ideal candidate thrives in autonomy and is self-directed to drive work to outcomes in an ambiguous, rapidly changing environment, including planning, sequencing, and communicating progress/risk.

• Establish and uphold the goals and expectations that support the fulfillment of cultural and business objectives.
• Operate with a team-first mindset, committed to ensuring the overall success of the team and organization.
• In collaboration with the Part D business team and BI engineers, lead data engineering solutions (design, data movement, orchestration, optimization) for prioritized business requests impacting organizational and enterprise initiatives.
• In Databricks: design, build, maintain, and support the data analytics platform, ensuring the platform is optimized for performance and the right tools are being used for the right jobs.
• Work collaboratively with teammates to optimally apply data warehouse, cloud, and BI architecture, normalization, and data visualization techniques to deliver solutions.
• Create and impact data scale solutions at the QCSA/Enterprise level to meet business needs.

• 7+ years of experience in data engineering, with a focus on building scalable data pipelines and architectures.
• Strong aptitude and motivation to grow into enterprise cloud platforms and technology as part of analytic modernization and enterprise alignment.
• Experience working in Databricks.
• Ability to analyze data to solve a wide variety of business problems, design and develop appropriate data movement and orchestration tools, and code to support strategic direction.
• Extensive understanding of star schema, snowflake schema, data architecture, and design principles.
• Strong SQL database design, optimization, ETL design, and execution (or similar orchestration).
• Ability to develop modular, reusable code and tools.
• Expertise in T‑SQL, Python, PySpark, and pySQL languages.
• Ability to thoroughly test and clearly document code.
• Review the work of and advocate best…