data
Posted 2 days agoDirector of Analytics Engineering
at Ennoble Care
United StatesOn-site
Responsibilities
- Strengthen and expand our analytics data warehouse infrastructure
- Lead AI-augmented engineering adoption: 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)
- 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
- We’re hiring a hands-on technical leader to strengthen and expand Ennoble’s ACO analytics data infrastructure.
- We expect this person to bring engineering rigor and AI-augmented practices to our team.
- This candidate will be our internal thought-leader on AI-augmented data engineering: LLM coding assistants, automated QA, autonomous data-quality agents, intelligent cross-source reconciliation, MCP servers as needed, and conversational query layers.
- requirements (BAA scope, Microsoft Entra identity scoping, PHI/PII suppression, Azure SQL firewall)
- 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
- SQL & Python: Expert T-SQL; Python proficient for ingestion utilities, internal web apps, and AI-agent tooling Data modeling:
- Experience designing unified/canonical models across heterogeneous source systems
- Healthcare data: 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 as a visible thought-leader on AI practices for a small team DevOps:
- Experience integrating pipelines with CI/CD, version control, branching strategies (dev → test → prod), DACPAC or equivalent schema-migration tooling, and managed-identity-based deployment