Principal Engineer

POSITION DUTIES & RESPONSIBILITIES

Architect 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.

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)

Preferred: 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