Qualified Health PBC

Qualified Health PBC

Director, Data Modernization

United States - Remote · Director

Sponsorship not specified$15k-$25kDetected 41 days ago
PythonGitSnowflakeDatabricksAzureTerraformCI/CDGitHub ActionsData EngineeringLLMsCybersecurityComplianceProject ManagementHIPAAEpicHL7/FHIREHR/EMRLeadership

About the role

  • The Director of Data Modernization is the technical architect and engineering leader across all of Qualified Health's data modernization engagements.
  • 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.

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
  • 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
  • Collaborate with Data Mapping Analysts on source data assessment and validation
  • You lead by example - reviewing code, pairing with engineers on tough problems, and shipping your own work alongside the team

Requirements

  • 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)

Nice to have

  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • A Master's degree is preferred.
  • Preferred Skills:

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
  • Client Presence: You're comfortable in a room with a health system CIO or VP of IT, explaining technical decisions in business terms
  • Technical Environment:
  • Our data infrastructure is built on modern cloud technologies including:
  • Azure Databricks + Data Factory (plus Fabric and Snowflake integrations)
  • PySpark for distributed data processing
  • GitHub Actions + Terraform for CI/CD and Infrastructure as Code
  • Python with type-safe patterns and modern frameworks

Compensation

  • Pay & Benefits: The pay range for this role is between $165,000 and $200,000, and will depend on your skills, qualifications, experience, and location.

Benefits

  • This role is also eligible for equity and benefits.
  • Join our mission to revolutionize healthcare with AI.
  • 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.
  • It's an opportunity to build the future of AI in healthcare, solve complex challenges, and make a lasting impact on patient care.
  • Join us in shaping the future of healthcare.
  • Client-facing experience - comfortable leading technical conversations with health system IT teams
  • You're comfortable in a room with a health system CIO or VP of IT, explaining technical decisions in business terms
  • You maintain engagement playbooks and architecture patterns so the next engagement benefits from what you learned in the last one

This listing is sourced directly from Qualified Health PBC's careers page and normalized into a canonical job model.