Sr. Director, Clinical Data Engineering

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.

Healthcare data engineering practice. 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.

Analytics and transactional data pipelines. 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.

Healthcare data domain depth. Bring deep expertise in how healthcare data is accessed and structured in practice — Epic Clarity and Caboodle, FHIR APIs, claims and eligibility data (EDI, 837/835, ICD-10, CPT/HCPCS, payer adjudication, risk adjustment). Newfire has healthcare data expertise across the organization; you should be among the most knowledgeable people in the company on the practical mechanics of getting data out of source systems and making it useful.

Technical execution. 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.

Commercial partnership. 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. You won't carry a sales quota, but your expertise should directly inform how we identify and pursue data-intensive work.

Technical leadership on engagements. 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.

Team development. 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.

AI and modern data practices. Healthcare data engineering is being reshaped by AI — from pipeline automation to LLM-powered extraction from unstructured clinical documents. Bring a current perspective on where these capabilities are production-ready and integrate them into Newfire's approach where they add real value.

Newfire Global is committed to consistent compensation practices across our organization. Total compensation for this role is market driven, with a salary of $229,000 - $280,000 with a 15% target bonus. Base pay is ultimately determined based on a candidate's skills, expertise, and experience. We also offer a comprehensive benefits package including medical, dental & vision coverage, health spending accounts, voluntary benefits, leave of absence policies, Employee Assistance Program, 401(k) program with employer contribution, Flexible work schedules and time-off policy, as well as company equipment for all new full-time US-based remote employees.  

 
Offers are contingent upon successful completion of a background screening. Background screenings will be conducted in compliance with applicable laws and will not be initiated without your consent.

Newfire Global is an Equal Employment Opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status, sexual orientation, gender identity or expression, marital status, genetic information, or any other characteristic protected by law.