Lead Engineer, Big Data

JOB SUMMARY:  

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. 

ESSENTIAL JOB DUTIES:  

• 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 capabilities, engineering progress, and technical concepts effectively to business leadership. 

REQUIRED QUALIFICATIONS:  

• Seven (7) or more years of professional experience designing and building production-grade software, analytics, or operational data platforms. 

• Demonstrated experience designing configurable operational platforms, workflow engines, decision engines, state machines, or comparable business applications with strong governance, auditability, version control, and long-term maintainability. 

• Expert proficiency with Python, SQL, and modern cloud analytics platforms such as Azure Databricks or equivalent technologies. 

• Proven ability to gather requirements directly from business stakeholders and translate complex operational needs into scalable technical solutions. 

• Experience owning production systems, including deployment, monitoring, incident response, and ongoing operational support. 

• Experience working with healthcare data, including claims, encounters, eligibility, medical records, provider, or clinical data. 

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or equivalent practical experience. 

PREFERRED QUALIFICATIONS:  

• Experience supporting Risk Adjustment, Quality, Care Management, Population Health, Utilization Management, or other regulated healthcare operations. 

• Familiarity with CMS Risk Adjustment methodologies, HCC coding models, RADV, HHS IVA, or related regulatory programs. 

• Experience building configuration-driven or metadata-driven software platforms. 

• Experience designing systems requiring deterministic execution, complete audit trails, and regulatory traceability. 

• Experience implementing workflow orchestration, business process automation, or operational decision-support platforms. 

• Familiarity with modern Generative AI technologies and AI-assisted software development. 

• Experience mentoring technical staff and establishing engineering standards within a business organization.