Lead Data Platform Engineer - Enterprise Analytics & AI

The Lead Data Engineer will design, build, and maintain the organization’s enterprise data platform, leading the technical implementation of data pipelines, warehouses, and analytics infrastructure that powers business intelligence, reporting, and advanced analytics across the PBM and Pharmacy organization. This hands‑on technical leadership role sets data engineering standards, mentors team members, and partners with business and technology stakeholders to deliver trusted, well‑governed, and timely data products.

• Design and build scalable, reliable data pipelines that ingest, transform, and load data from operational systems, clinical platforms, claims, and third-party sources.
• Develop and maintain the enterprise data warehouse, data lake, and analytical data models that serve reporting and analytics use cases.
• Design data services and event‑driven integration patterns that enable scalable downstream consumption by analytics platforms, operational systems, APIs, and AI‑enabled applications.

• Serve as the senior technical authority for data engineering, setting standards for code quality, pipeline design, data modeling, and testing across the team.
• Lead technical planning for data engineering initiatives, breaking work into well‑scoped tasks and coordinating delivery across team members.
• Mentorship and
  
  Collaboration:
• Provide technical direction and code review for data engineers, ensuring consistency, quality, and adherence to standards.
• Participate in hiring and onboarding of data engineering team members, including technical interviews and skills assessments.
• Mentor data engineers across all levels, fostering a culture of technical excellence, knowledge sharing, and continuous improvement.

• Partner with the Architecture team to define and implement data architecture, including data warehouse models, data lake structures, and integration patterns.
• Apply dimensional modeling, normalization, and modern data modeling techniques (e.g., Kimball, Data Vault) to support analytics and reporting requirements.

• Optimize query performance, storage costs, and pipeline runtime across the data platform.
• Implement observability, monitoring, and alerting for production data pipelines, and partner with operations to ensure timely incident response.
• Identify reliability, data quality, and performance risks and develop mitigation strategies to ensure platform stability and data trustworthiness.

• Implement controls to ensure compliance with HIPAA, PHI/PII handling, and other regulatory requirements applicable to the healthcare and pharmacy sectors.
• Partner with security and compliance teams on access control, encryption, audit logging, and data lineage for sensitive data assets.
• Design and enable scalable, governed data access patterns that support AI/ML systems, intelligent automation, and emerging agentic workflows, including structured, semantic, and real‑time data consumption patterns.

• Partner with analytics, business intelligence, and product teams to understand data needs and deliver fit‑for‑purpose datasets, models, and pipelines.
• Translate business and reporting requirements into well‑designed technical data engineering solutions.

• Evaluate and recommend new data engineering tools, frameworks, and cloud services that improve productivity, scalability, or cost‑efficiency.
• Stay current on advances in cloud data platforms, lakehouse architectures, streaming technologies, and AI/ML data infrastructure.

• Provide production support for critical data pipelines, participating in on‑call rotations as needed.
• Diagnose and resolve complex data quality, performance, and integration issues spanning multiple systems and platforms.

• Implement data quality validation, backup and recovery, and pipeline monitoring to ensure continuous data delivery.
• Recommend tooling and infrastructure needed to support the enterprise data platform.
• Prepare and review data platform health metrics, pipeline performance reports, and…