Director, Data Modernization
Qualified Health Pbc
| Company | Qualified Health Pbc |
| Category | Data & Analytics |
| Location | United States - Remote |
| Remote | Remote |
| Employment | Not stated |
| Level | Director |
| Salary | Not stated by the employer |
| Posted | 11 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
Transform healthcare with us.
At Qualified Health, we're redefining what's possible with Generative AI in healthcare. Our infrastructure provides the guardrails for safe AI governance, healthcare-specific agent creation, and real-time algorithm monitoring — working alongside leading health systems to drive real change.
This is more than just a job. It's an opportunity to build the future of AI in healthcare, solve complex challenges, and make a lasting impact on patient care. If you're ambitious, innovative, and ready to move fast, we'd love to have you on board.
Join us in shaping the future of healthcare.
JOB SUMMARY:
The Director of Data Modernization is the technical architect and engineering leader across all of Qualified Health's data modernization engagements. You'll walk into a health system's data environment — which might be anything from a well-maintained EHR reporting database to a tangle of flat files and manual SFTP processes — and design the modern cloud data platform that will replace it.
This is the highest-leverage work on the team. Every health system you modernize from legacy connectivity to a modern data sharing pattern reduces their ongoing integration effort dramatically. You're not just building infrastructure — you're removing the single biggest bottleneck to AI deployment across our entire partner base.
You'll own the technical architecture playbook, make design decisions across concurrent engagements, lead a team of data engineers and cloud/infrastructure engineers, review all engineering output, and personally drive the most complex builds. The Engagement Manager handles the partner relationship and project management — you handle the engineering. Together, you run the engagement.
KEY RESPONSIBILITIES:
- Own the technical architecture for modernization engagements: Azure Databricks design, data landing zones, networking, security controls
- Lead and develop a team of data engineers and cloud/infrastructure engineers
- Lead technical discovery and source data assessments for new engagements
- Review and approve all engineering deliverables across concurrent engagements
- Personally drive architecture and build for the most complex engagements
- Set engineering quality standards and drive architectural decisions across the modernization practice
- Maintain and evolve the technical engagement playbook
- Ensure environments meet handoff criteria for the Client Integration team
- Collaborate with Data Mapping Analysts on source data assessment and validation
- This role requires travel to healthcare client sites for engagement kickoffs, technical discovery sessions, and key milestones. Candidates should be prepared for 15-25% travel, depending on active engagements.
REQUIRED QUALIFICATIONS:
- Education: Bachelor's degree in Computer Science, Engineering, or a related field. A Master's degree is preferred.
- Required Experience:
- 6+ years in data engineering or data platform roles, with demonstrated technical leadership
- Deep Azure expertise: Databricks, ADLS2, Azure networking, managed identities, Terraform/Bicep
- Experience with data platform design and deployment (greenfield builds)
- Client-facing experience — comfortable leading technical conversations with health system IT teams
Preferred Skills:
- Familiarity with EHR data sources and clinical data models (Epic Clarity/Caboodle preferred)
- Experience with data sharing protocols (Delta Share, Fabric External Sharing, or similar)
- Background in consulting, professional services, or data platform implementation
- Experience designing data architectures for HIPAA-regulated environments
- Track record of managing technical quality across concurrent engagements
- Architectural Judgment: You can walk into an unfamiliar data environment, assess what exists, identify what's missing, and design a target state architecture that is both technically sound and achie
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