About Us: At RELI Group, our work is grounded in purpose. We partner with government agencies to solve complex challenges, improve public health, strengthen national security, and make government services more effective and efficient. Our team of over 500 professionals brings deep expertise and a shared commitment to delivering meaningful outcomes. Behind every solution is a group of experts who care deeply about impact—whether we’re supporting data-driven decisions, modernizing systems or safeguarding critical programs.

RELI Group is seeking a Lead Data Engineer to provide technical leadership for the CMS MIDAS modernization initiative. Following the migration to Databricks, MIDAS is entering a critical platform hardening phase focused on standardization, architectural discipline, CI/CD reconstruction, data model unification, and automation of data transformation validation.

This role serves as the technical lead across multiple Scrum teams, guiding a distributed team of Data Engineers and establishing enforceable engineering standards. The Lead Data Engineer is responsible for defining how the work is executed — not just completing individual tasks.

The objective is to engineer a sustainable, scalable lakehouse foundation that prevents operational fragility and enables future optimization, advanced analytics, and AI capabilities.

Key Responsibilities:

• Technical Leadership & Vision
• Define and enforce engineering standards across all Databricks development efforts.
• Lead implementation of a formal Bronze / Silver / Gold medallion architecture.
• Drive alignment toward a unified data model across domains.
• Establish code quality, naming conventions, reusable frameworks, and architectural patterns.
• Serve as the technical escalation point for complex data engineering challenges.
• Execution Across Scrum Teams
• Oversee Data Engineers embedded across multiple Scrum teams.
• Provide architectural guidance during backlog refinement, sprint planning, and design sessions.
• Identify cross-team dependencies and proactively remove technical blockers.
• Ensure consistent Definitions of Done that include testing, documentation, and deployment discipline.
• Partner with Release Management and DevOps to align data engineering with deployment workflows.
• CI/CD & Governance Modernization
• Lead reconstruction and enforcement of CI/CD pipelines across lower environments (Dev/Test/Stage).
• Establish test data strategies to support lower-environment validation and automated regression testing.
• Rationalize GitHub repository structure, branching strategy, and version control governance.
• Implement automated data transformation testing frameworks.
• Strengthen observability, lineage, and data quality controls.
• Strategic Platform Stewardship
• Partner with Program Leadership to define phased modernization roadmaps.
• Sequence foundational work prior to optimization and AI enablement initiatives.
• Ensure technical debt is surfaced, prioritized, and reduced methodically.
• Advise leadership on risk tradeoffs between speed and architectural integrity.