About this role.
H1 is seeking a Principal Analyst, Data Integration to lead the end-to-end process of evaluating and onboarding new data sources into its healthcare data platform. This senior individual contributor role bridges data, engineering, and product, working closely with cross-functional teams to define source schemas, field mappings, and entity resolution rules. The ideal candidate has 8-12+ years of experience in data-focused roles at healthcare or life sciences companies and has personally owned full data integration lifecycles. Responsibilities include scoping, QA, documentation, and post-integration data quality resolution, ensuring that new sources deliver value to clients. This role requires strong judgment on integration risk and clear communication across technical and non-technical stakeholders.
Role DNA
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Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
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Core skills
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Cover letter sample
Dear Hiring Manager,
I am writing to express my strong interest in the Principal Analyst, Data Integration position at H1. With over 10 years of experience in data integration and healthcare data management, I have successfully led end-to-end onboarding of complex data sources, from evaluation and mapping to QA and launch. I am particularly drawn to H1's mission of improving healthcare outcomes through data, and I believe my expertise in entity resolution, field mapping, and cross-functional collaboration aligns perfectly with this role.
In my previous role at a leading health IT firm, I owned the full lifecycle of multiple data integrations, including scoping, schema design, and product QA, while working closely with engineering and product teams to deliver high-quality data to clients. I am confident that my ability to translate commercial needs into technical specifications and my rigorous approach to data quality will add immediate value to H1's Data & Research team.
Thank you for considering my application. I look forward to the opportunity to discuss how my skills can contribute to H1's continued growth and impact.
Sincerely,
[Your Name]
Sample interview questions
First, I would assess the source's schema, coverage, freshness, and legal constraints. I'd check if the data aligns with our product needs and identify potential risks in entity resolution. Then I'd produce a scoping assessment detailing what the source can and cannot deliver, and present it to stakeholders to decide whether to proceed.
I would set clear expectations early by communicating the source's capabilities and limitations in non-technical terms. If necessary, I would work with the client-facing team to adjust requirements or find alternative data sources. Throughout the process, I maintain transparent documentation to avoid misunderstandings.
In a previous role, we discovered field-level fill rates were lower than expected post-launch. I led a root cause analysis, coordinating with engineering to trace the issue to a mapping error. We corrected the mapping and implemented automated anomaly detection to catch similar issues in the future.
I have extensive experience with rule-based and probabilistic entity resolution. I start by defining clear matching rules based on common identifiers and attributes. I then validate matches through sampling and manual review. For ongoing accuracy, I set up QA checks to compare match rates and flag anomalies.
I evaluate each request based on business impact, technical feasibility, and data quality. I collaborate with product managers to align on priorities and create a roadmap. I also communicate trade-offs clearly so that teams understand the timeline and resource constraints.
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
– 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
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