Director of Data Engineering

Ennoble Care is a mobile primary care, palliative care, and hospice service provider. They are seeking a hands-on technical leader to strengthen and expand their ACO analytics data infrastructure, owning the design of a unified data platform and ensuring data accuracy and reconciliation.

Responsibilities:

• Strengthen and expand our analytics data warehouse infrastructure
• Apply AI-augmented engineering in daily work: codify patterns for LLM coding assistants (e.g., Claude Code under BAA), agentic QA, autonomous data-quality monitoring agents, and conversational query layers
• Own data accuracy and reconciliation — build validation, monitoring, and audit infrastructure that catches issues before they reach reporting or CMS submissions — extending our ~158-object audit and observability framework (ADF run logging, MBI crosswalk QA, DPC parity harness, transactional refresh with auto-rollback)
• Partner with analytics leads (MSSP, ACO REACH/LEAD, GUIDE, Quality, Dashboards) to ensure infrastructure meets internal and external reporting requirements and advanced downstream modeling needs
• Lead large-scale analytics platform and web app implementation, integration, and maintenance
• Collaborate closely with IT and Engineering on tooling standards, organizational approvals, and security requirements (BAA scope, Microsoft Entra identity scoping, PHI/PII suppression, Azure SQL firewall)

Requirements:

• Strong hands-on experience with cloud-based analytics infrastructure and production data pipelines
• Platform: Microsoft Azure — incl. Azure SQL, Azure Data Factory, Azure DevOps, GitHub, Azure Blob Storage, Azure Databricks experience a strong plus
• Expert T-SQL; Python proficient for ingestion utilities, internal web apps, and AI-agent tooling
• Experience designing unified/canonical models across heterogeneous source systems
• Demonstrated validation, reconciliation, and audit framework design — especially where discrepancies have material downstream impact
• Experience integrating pipelines with CI/CD, version control, branching strategies (dev → test → prod), DACPAC or equivalent schema-migration tooling, and managed-identity-based deployment
• Able to translate infrastructure decisions and engineering workflows into business impact for non-technical stakeholders
• Familiarity with Medicare claims (CCLF, DPC, BCDA or similar) and/or risk-based contracting strongly preferred
• AI fluency: Track record using LLM-based tools (e.g., Claude Code, Copilot) to accelerate data engineering; comfortable using these tools to ship reliable engineering work on a small team