Principal Analyst, Data Integration

At H1, we believe access to the best healthcare information is a basic human right. Our mission is to provide a platform that can optimally inform every doctor interaction globally. This promotes health equity and builds needed trust in healthcare systems. To accomplish this our teams harness the power of data and AI-technology to unlock groundbreaking medical insights and convert those insights into action that result in optimal patient outcomes and accelerates an equitable and inclusive drug development lifecycle.  Visit h1.co to learn more about us.

 

The Data & Research team is responsible for the end-to-end lifecycle of H1's data — from source acquisition and onboarding through normalization, entity resolution, and product delivery. Working closely with Engineering, Product, and client-facing teams, this group ensures that every data source H1 adds is evaluated rigorously, integrated accurately, and delivered in a way that creates real value for the healthcare and life sciences organizations that rely on our platform.

 

As a Principal Analyst, Data Integration, you will own the end-to-end process of evaluating, scoping, and onboarding new data sources into H1's platform. This is a senior IC role at the intersection of data, engineering, and product — the connective tissue between raw data acquisition and what ultimately ships to clients. You will work across Data & Research, Engineering, and Product to define what a new source is, how it maps to H1's schemas, what it can realistically deliver, and what it can't. You will also work directly with client-facing teams to gather requirements before integration decisions are made, translating commercial needs into data specs and data constraints back into product expectations.

You will:
- Lead structured evaluation of new data sources from scratch — assessing schema, coverage, freshness, legal constraints, and fit against H1's product needs before any engineering work begins
- Own field mapping from source to H1's bronze/silver/gold layers, producing data dictionaries, entity definitions, and structural guidance for downstream teams
- Partner with engineering and Data Lake to define ingestion requirements, entity resolution rules, and refresh cadences for new sources
- Gather requirements from client-facing teams and translate them into integration specifications; serve as the authoritative voice on what a new source can and cannot deliver before product commitments are made
- Shepherd each source end-to-end: scoping → QA → entity matching → product launch, including product QA and communicating source capabilities and limitations to product and enablement partners
- Work with the Insights team to develop new taxonomies and QA mechanisms for novel data types
- Define acceptance criteria and lead QA validation including field-level fill rates, count comparisons, and cycle-over-cycle anomaly detection
- Investigate and resolve data quality issues post-integration, coordinating with DART and engineering as needed
- Hand off to the maintaining team with complete mapping documentation; you own onboarding, not ongoing maintenance
- Produce and maintain documentation other people actually use — across scoping assessments, field mapping specs, and post-mortems

You are a senior data professional who has personally owned the full lifecycle of a data integration — not just contributed to one. You are comfortable working across ambiguous, novel data structures and can ramp quickly on new source types each quarter. You operate with strong judgment about where integration risk lives, and you communicate clearly to both technical partners and client-facing stakeholders about what data can and cannot do.

 

- 8–12+ years in data-focused roles at healthcare data companies, pharma/biotech data vendors, health IT firms, or equivalent
- Demonstrated end-to-end ownership of data integrations built from scratch — scoping, field mapping, QA, and handoff — with documentation to show for it
- Healthcare or life sciences domain context required; ability to ramp on new datasets and source types each quarter without needing deep subject matter expertise upfront
- Analytical fluency to assess data quality; hands-on experience with tools such as VBA, R, or SPSS; SQL a plus but not a primary requirement
- Familiarity with data lake architectures (bronze/silver/gold or equivalent) and how raw data moves through normalization and entity resolution to a product-ready state
- Experience gathering requirements from client-facing stakeholders and translating them into data or product specifications
- Experience at a B2B data company where you understood how external clients consumed your data and where client retention drove decisions
- AWS infrastructure familiarity (Athena, S3, Glue) at a query and inspection level preferred
- Comfort working in Jira or Monday in a ticket-based workflow
- Exceptional written communication — your documentation is legible, maintained, and actually used

 

 

This role pays $145,000 to $170,000 per year, based on experience, in addition to equity.

 

Anticipated role close date: 08/25/2026