Job SummaryThis is the role where the data actually moves. As a Senior Forward Deployed Engineer on the Data Modernization team, you own the platform-specific build that takes a health system from legacy connectivity - flat files, manual SFTP, a half-finished Clarity database - to a modern, AI-ready lakehouse that can serve our agentic AI workflows at full speed.
You are the deep platform expert for your stack. During an active engagement, you serve as the platform-specific technical lead under the Principal Solutions Architect: you own the ingestion, the medallion architecture, the governance configuration, and the data-sharing pattern on your platform. Between engagements, you sustain our production environments, build the accelerators and reusable IP that make the next engagement faster, support pre-sales technical discovery, and cross-train on the other platforms so the team stays flexible.
These are time-boxed, high-stakes builds. A greenfield foundation goes from zero to a live AI workflow in roughly ten weeks; an acceleration engagement folds hundreds of Clarity tables into an existing lakehouse on weeks-to-months timelines. You9ll ship production-grade work in a regulated environment, where 44done44 means it9s governed, documented, and ready to hand to the integration team - not just that the pipeline ran once.
Key Responsibilities- Work forward-deployed inside the customer9s environment: partner directly with their data and IT teams, present design decisions and progress to their technical leadership, and represent QH on-site during kickoffs and key milestones
- Own platform-specific architecture and build for your stack during active engagements, as technical lead under the Principal Solutions Architect
- Design and implement ingestion from EHR and source systems (Epic Clarity / Caboodle, FHIR, ERP, scheduling, claims) into a medallion lakehouse
- Build and harden change-data-capture, transformation, and orchestration pipelines that meet engagement timelines
- Configure governance, access control, and the data-sharing pattern that hands clean, AI-ready data to QH9s platform (Delta Sharing, Fabric External Sharing, Snowflake Reader Accounts, or equivalent)
- Sustain production environments handed off from prior engagements, and develop reusable accelerators and IP that compress the next build
- Support pre-sales technical discovery and source-data assessment alongside the Principal SA
- Ensure every environment meets handoff criteria for the Client Integration team - governed, documented, reproducible
- Cross-train on the other two platforms to keep the team flexible across single- and multi-engagement states
Required Qualifications- 8+ years in data engineering or data platform roles, at a Senior or Staff IC level
- Client-facing maturity - comfortable working on-site in a customer9s environment and presenting technical work to their data and IT leadership
- Deep, hands-on expertise in at least one of Databricks, Snowflake, or Microsoft Fabric (see platform note above)
- Has shipped production data workloads in a regulated environment (HIPAA, HITRUST, or comparable)
- Strong in Python and distributed data processing (PySpark or equivalent), plus SQL and modern transformation tooling
- Comfortable as the sole platform expert on an engagement - you can own a build, not just contribute to one
- Infrastructure-as-code fluency (Terraform) and CI/CD discipline (GitHub Actions)
Platform-Specific Depth (own one)- Databricks: Delta Lake, Unity Catalog, Delta Sharing, Delta Live Tables, Photon. Bonus: production Databricks experience inside a health system or Databricks partner consultancy.
- Snowflake: Snowpark, Reader Accounts, Streams & Tasks, Dynamic Tables, Snowpipe, strong dbt fluency. Bonus: Epic Clarity inside Snowflake.
- Microsoft Fabric: OneLake, SQL Server Mirroring for CDC, Fabric Data Factory, Fabric External Sharing, Iceberg shortcuts. Bonus: prior Azure Synapse / ADF / Databricks-on-Azure background. (The rarest and most sought-after of the three - Fabric is the newest platform.)
Ideal Experience- Resident / customer-success solutions architect or engineer from a cloud data platform vendor (Databricks, Snowflake, Microsoft FastTrack / CSU)
- Senior data engineer from a health system running on your platform, or from a platform-partner consultancy
- Familiarity with EHR data models and the realities of on-prem-to-cloud CDC
- Background in consulting, professional services, or data platform implementation in regulated industries (healthcare strongly preferred; fintech a strong adjacent)
Desirable Skills- Ownership: You9re the one person on the engagement who deeply knows this platform, and you carry that weight without needing a second set of hands on every decision.
- Pragmatism: You know the difference between architecturally ideal and deliverable-in-ten-weeks, and you optimize for the latter without creating technical debt.
- Reusability mindset: You build the second engagement9s accelerator while delivering the first, because you9ve felt the cost of bespoke-everything.
- Clinical-data literacy: Clarity and Caboodle don9t scare you; you understand why health system data is messy and you9ve untangled it before.
- Cross-platform curiosity: Your depth is in one stack, but you9re glad to learn the other two so the team can flex across engagements.
Technical Environment- Databricks (primary), Microsoft Fabric, and Snowflake - fully platform-agnostic on acceleration engagements
- PySpark and Python with type-safe patterns and modern frameworks
- GitHub Actions + Terraform for CI/CD and Infrastructure as Code
- Healthcare data formats including FHIR, Epic Clarity / Caboodle, and other EHR schemas
- HIPAA / HITRUST-regulated cloud environments on Azure and AWS
Pay & BenefitsThe base pay range for this role is between
$180,000 and $230,000. Final offer depends on your skills, qualifications, experience, platform specialization, and location. This role is also eligible for equity, benefits, unlimited PTO.
