Lead Engineer, Big Data

The Lead Analytics Engineer designs and supports business-owned analytic capabilities that power critical Risk Adjustment functions including member targeting, workflow management, operational reporting, and regulatory support.

Through close partnership and collaboration with corporate IT teams, this position supports development of configurable solutions that enable analysts to safely manage business rules, execute complex operational workflows, and maintain complete auditability across business processes. The successful candidate combines strong technical expertise with business orientation partnering directly with analysts to translate evolving operational needs into scalable, reliable solutions while owning the full engineering lifecycle from discovery through production support.

Operational Platform Ownership

Design, develop, deploy, and continuously evolve business maintained operational platforms supporting Risk Adjustment workflows, including targeting, medical record review, reporting, and operational case management.

Own the complete development lifecycle including solution design, implementation, testing, deployment, production support, monitoring, and continuous improvement.

Build modular, configuration-driven solutions that remain scalable, maintainable, and adaptable as business needs, membership, and regulatory requirements evolve.

Maintain and enhance existing production platforms while balancing operational stability with ongoing feature development.

Business Process Engineering

Partner directly with Risk Adjustment analysts and business leadership to understand operational workflows and identify opportunities for process improvement.

Translate complex operational processes into configurable systems that allow business logic to evolve without frequent engineering changes.

Design deterministic workflow and decision engines that provide complete auditability, traceability, and regulatory transparency.

Continuously improve business operations through automation, standardization, and thoughtful platform design.

Configuration-Driven Systems Development

Design metadata-driven platforms that separate business configuration from application code, enabling governed operational changes without core software modifications.

Build reusable frameworks for business-rule execution, validation, testing, auditing, version management, and workflow orchestration across multiple Risk Adjustment functions.

Develop tooling that enables analysts to safely extend platform capabilities within defined engineering guardrails while preserving production reliability.

Engineering Excellence

Apply modern engineering practices including version control, automated testing, peer review, structured release management, and production monitoring.

Develop high-quality Python and SQL solutions emphasizing performance, maintainability, readability, and long-term supportability.

Optimize platform performance to support growing data volumes and increasingly sophisticated operational workflows.

Establish standards that enable analysts to safely contribute enhancements through governed development practices while ensuring platform quality and sustainability.

Applied AI & Intelligent Automation

Engage with corporate AI assets to apply artificial intelligence to improve analyst productivity, software development, operational support, and business decision-making.

Build AI-enabled capabilities that simplify interaction with operational systems, documentation, and business knowledge while accelerating solution delivery.

Cross-Functional Business Partnership

Serve as the primary analytics engineering partner to Risk Adjustment, recommending technical solutions that improve operational effectiveness and scalability.

Collaborate with compliance, audit, clinical, and technology teams to ensure platforms satisfy business, regulatory, and operational requirements.

Communicate platform