Principal Engineer, Data Architecture

Position Duties & Responsibilities

• Arcitect the Healthcare Data Model: Design the core schemas and data models that define our business, including how we model complex, systemic concepts like Attribution, Risk, and Claims Adjudication to ensure historical accuracy, scalability, and query performance.
• Define the Technology Standards & Strategy: Set the overarching strategy for our data stack (Snowflake, real-time streams, etc.) and dbt environments, dictating our approach to data schema, reporting schema, and stream/transactional use of data, including dimensional modeling (Kimball/Star Schema), denormalization vs. normalization, and Medallion architecture progression.
• Data Usability and Access: Design and implement the deterministic and probabilistic matching logic to assign unique identifiers to patients coming from highly fragmented external sources such as HIEs, external EMRs, and payers.
• Bridge Engineering and Clinical Operations: Act as the primary strategic interface between Clinical/Business Operations and Engineering, interviewing stakeholders to uncover the "why" behind their requests and translating ambiguous business needs into precise architectural specifications.
• Domain Leadership & Mentorship: Act as the deep subject matter expert on healthcare data for the engineering team, mentoring engineers on healthcare domain nuances (e.g., why a reversal claim behaves differently than a void) and raising the collective technical bar for system design.
• Technical Ownership & Execution: Drive complex initiatives from inception to delivery, interpreting asks from clinical and business stakeholders and working with the engineering team to design and deliver the optimal data architecture.

Required DESIRED PROFESSIONAL SKILLS & EXPERIENCE

• Deep subject-matter expertise in healthcare "payvider" data domains, both provider- and payer-side, with fluency in EDI, FHIR, ADT, and other standards used in healthcare, and how this data is aggregated and modeled to support execution
• Proven experience designing highly scalable data platforms from scratch, with a deep understanding of how column-oriented databases (Snowflake) and real-time processing work under the hood, and how to design schemas that optimize partition pruning and compute costs at scale
• Fluency in structuring a massive dbt project for enterprise scale, including macro-architecture, incremental strategies, and dependency management, while balancing quality management rigor with velocity
• Ability to write performant, readable, complex SQL and read/review Python pipelines, plus the technical influence to coach other engineers through systemic standards and architectural reviews

Preferred

• Deep subject-matter expertise in healthcare data domains, including fluency in processing 837 files, modeling the claim lifecycle (adjudication, reversal, denial), and handling provider data nuances (NPI vs. TIN)
• Proven experience designing dimensional models (Kimball/Star Schema) from scratch, with judgment on when to denormalize for performance versus normalize for integrity

Our Technology Stack

• Transformation: dbt (Core/Cloud)
• Warehouse: Snowflake
• Ingestion: Fivetran, Python (custom)
• Orchestration: Airflow / Dagster
• BI/Semantic Layer: Omni

What We Offer

• Competitive salary and incentives
• Generous PTO
• 10 paid holidays
• Medical, Dental, and Vision Insurance
• 401(k) Investment Plan
• Company Equity
• Professional development and growth opportunities

About the Role

Harbor Health is rebuilding the operating system of healthcare. The Harbor Data Systems (HDS) team contributes to this by building HarborOS, the central nervous system of our clinical operations, tying together information from different clinical sources to drive optimal outcomes for our members — data is our product. We are looking for a Principal Engineer, Data Architecture to lead the design of foundational data structures and ontologies that will help us transform healthcare. You will not just move data; you will define how our business understands it, taking raw chaos — claims, ADT, disparate EMR feeds — and architecting the trusted data models and master indexes that power our patient care, risk adjustment, and financial decisions.