Sr. Director, Clinical Data Engineering

Newfire Global Partners•Boston, MA

7d•$229,000 - $280,000•Remote

About The Position

This role builds and leads Newfire's clinical data engineering capability. You'll be responsible for evolving our approach to healthcare data architecture, developing reusable patterns for the data engineering problems our clients face, and ensuring Newfire can credibly lead work involving claims data, EHR data access, and analytics infrastructure. You'll work closely with our strategic consulting and commercial teams to identify opportunities where data engineering expertise creates client value, and you'll step into engagements to provide technical leadership when the situation calls for it. This is a hands-on leadership role. We need someone who can design a data architecture for a client and also build a pipeline front to back when that's what the situation requires. You should be deeply comfortable with modern data platforms — Databricks, Snowflake, dbt — and have strong opinions about how to use them well in healthcare contexts. You'll lead a data engineering team that drives both analytics and transactional pipeline work on behalf of clients, and you'll bring deep practical knowledge of how healthcare data is actually accessed — from Epic Clarity to claims and eligibility feeds.

Requirements

Deep healthcare data domain expertise. You've worked extensively with claims and eligibility data and clinical/EHR data. You understand how data is accessed in practice — Epic Clarity and Caboodle, FHIR APIs, claims feeds, eligibility files. You know the data models, coding systems, regulatory constraints, and quality challenges specific to healthcare.
Technically hands-on. You can build a data pipeline front to back. You're fluent in modern data platforms — Databricks, Snowflake, dbt — and have used them in production, not just evaluated them. SQL is second nature. You have strong opinions about data modeling, pipeline orchestration, and testing.
Practice leadership, not just technical delivery. You've built data engineering capability at an organizational level — defined standards, created reusable approaches, hired and developed teams. You can set direction for a practice area while staying close enough to the work to maintain technical credibility.
Consulting or multi-client experience. You've worked across multiple healthcare organizations and understand that data engineering looks different at a health system versus a payer versus a digital health company. You're comfortable with the variability of a consultancy environment.
Commercial instinct. You can recognize where data engineering expertise creates business value for a client and articulate that in a way that resonates with non-technical decision makers.
Pragmatic modernizer. You have strong opinions on modern data stack and cloud-native architecture, and you also know that most healthcare organizations are working with legacy systems, constrained budgets, and regulatory overhead. You meet clients where they are.
AI fluency. Working understanding of how AI intersects with data engineering in healthcare — unstructured data extraction, data quality automation, intelligent pipeline design — with honest assessment of what works today.

Responsibilities

Define and evolve Newfire's approach to clinical and claims data engineering. Establish best practices, reference architectures, and reusable patterns — data lake and warehouse design, ETL/ELT pipelines, data quality and governance frameworks, and analytics-ready data models for healthcare use cases.
Lead a data engineering team that builds and operates pipelines on behalf of clients — both analytics workloads (population health, quality measures, reporting) and transactional pipelines (claims processing, eligibility, real-time data flows). Own the technical quality and reliability of this work.
Bring deep expertise in how healthcare data is accessed and structured in practice — Epic Clarity and Caboodle, FHIR APIs, claims and eligibility data (EDI, 837/837, ICD-10, CPT/HCPCS, payer adjudication, risk adjustment).
Stay close to the work. Review architectures, debug pipeline issues, evaluate new tools. You should be the kind of leader who can sit down with a data engineer and work through a problem in Databricks or dbt, not just review it in a slide deck.
Work with strategic consulting and commercial leadership to identify where data engineering creates opportunities — in active engagements, in proposals, and in how we position capabilities to the market.
Step into client engagements to provide data architecture guidance, assess existing data infrastructure, or lead technical design when the engagement requires senior data engineering judgment.
Build and develop data engineering talent at Newfire — identifying skill gaps, mentoring engineers, and shaping how we recruit for data engineering capability. Coordinate with the strategic bench (Newfire Labs) to develop data engineering skills during bench time.
Bring a current perspective on where AI capabilities are production-ready and integrate them into Newfire's approach where they add real value.