Job Description

About the Role

Datavant is seeking a versatile, technically fluent, and commercially minded Product Manager to own Data Harmonization within our Life Sciences business. This role sits on the Data Utility team, which develops privacy, remediation, and harmonization capabilities that make health data more usable for research and analytics.

At Datavant, data harmonization means transforming disparate real-world datasets into standardized, quality-validated, privacy compliant, analytics-ready assets. The workflow includes profiling source data; mapping schemas and clinical concepts; normalizing formats, units, and vocabularies; validating outputs; managing exceptions; and producing traceable datasets aligned to a common or customer-defined data model and meeting the privacy requirements to be HIPAA de-ID. These capabilities must integrate cleanly with Datavant’s ingestion, privacy, expert determination, and downstream analytics workflows.

This is a zero-to-one product strategy and execution role, not simply a feature-delivery role. You will begin by defining the end-to-end harmonization workflow with customers and our Delivery team, including where software can automate work and where expert judgment remains necessary. You will then determine which capabilities Datavant should build, buy, or deliver through partners, leading structured vendor evaluations and proofs of concept.

As the strategy matures, you will turn repeatable delivery work into scalable software, reusable mapping assets, and a commercially viable product offering. You will partner closely with Engineering, Data Science, Delivery, Privacy, Platform, and Go-to-Market teams and work directly with customers’ data engineering, informatics, analytics, and research teams.

Success in this role will be measured by reducing the time and effort required to move from source data to analytics-ready outputs; increasing reuse of mappings and transformation rules; improving data quality, lineage, and reproducibility; reducing manual delivery effort and rework; and driving customer adoption.

What You’ll Do

• Own the product strategy and roadmap for data harmonization. Define the target users, priority use cases, product boundaries, business case, and phased path from service-supported workflows to scalable product capabilities.
• Define and own the end-to-end workflow. Map the process from source-data intake, profiling, and quality assessment through schema and semantic mapping, transformation, validation, privacy and remediation, and final analytics-ready delivery.
• Clarify how harmonization interacts with privacy and remediation. Define the sequencing, data contracts, and controls required so that privacy transformations preserve mapping integrity, analytical utility, and source-to-output traceability.
• Lead build, buy, and partner decisions. Establish evaluation criteria, assess vendors, run proofs of concept using representative datasets, and make defensible recommendations.
• Define the technical requirements and build reusable assets. Work with Engineering to spec the full pipeline: intake, quality checks, mapping, transformation, validation, and delivery (file, API, or data platform) with full traceability. Productize (or implement via a partner) the mapping templates, transformation rules, and validation logic into governed, reusable components, versioned and auditable, rather than artifacts rebuilt for every engagement. This could also include integration with external or internal systems.
• Design for real-world operational complexity. Define requirements for human review and exception management, schema drift, incremental data refreshes, failed transformations, quality thresholds, monitoring, and ongoing support.
• Engage customers directly. Understand customers’ source data, target models, use-cases and delivery constraints. Translate those needs into clear product requirements without allowing one-off requests to overwhelm the scalable product strategy.
• Partner with Data Science and Delivery to productize services. Identify the most repetitive and costly parts of current delivery workflows, establish baselines for manual effort and rework, validate proposed automation, and measure the product impact.
• Shape the commercial offering with GTM. Help define packaging, implementation models, pricing inputs, positioning, and sales enablement.
• Lead execution as the product matures. Serve as the Agile Product Owner by prioritizing the backlog, writing requirements and acceptance scenarios, managing dependencies, and partnering with Engineering through discovery, delivery, launch, adoption, and iteration.
• Define and track product performance. Establish metrics to track offering performance such as time to analytics-ready data, percentage of mappings and rules reused, automated mapping coverage, and customer adoption.

What You Bring

• 5+ years of product management or technical product ownership experience, preferably with data platforms, data integration, developer tools, enterprise workflows, or analytics products.
• Experience owning a complex product from discovery and strategy through delivery, launch, adoption, and ongoing improvement.
• Demonstrated experience making build-versus-buy-or-partner decisions, including vendor evaluation, technical due diligence, proof-of-concept design, business-case development, and total-cost-of-ownership analysis.
• Strong grounding in data integration and ETL/ELT concepts, including pipelines, schema mapping, transformation, APIs, metadata, lineage, validation, and batch or incremental processing.
• Enough technical fluency to review data dictionaries, source-to-target mapping specifications, SQL, API documentation, transformation logic, and data-quality results with engineers and data scientists. You are not expected to be a production engineer, but you must be comfortable working at this level of detail.
• Experience translating complex, expert-led, or services-heavy workflows into repeatable product capabilities and internal or customer-facing software.
• Familiarity with one or more health data models and standards, such as OMOP, CDISC/SDTM, or HL7 FHIR, and an understanding that common data models, research submission standards, and exchange standards serve different purposes.
• Strong analytical and product judgment, including the ability to define success metrics, test assumptions, evaluate tradeoffs, and make decisions with incomplete information.
• Experience working directly with enterprise customers and translating differing customer requirements into a coherent, scalable product strategy.
• Strong written and verbal communication skills, with the ability to explain complex technical decisions, keep open questions visible, and align stakeholders across Product, Engineering, Data Science, Delivery, Privacy, and GTM.
• Highly organized and comfortable managing ambiguity, dependencies, and competing priorities while driving decisions and maintaining momentum.
• Experience delivering enterprise-grade software and operating within Agile product-development teams.
• Passion for improving healthcare and making health data more usable, trustworthy, and valuable.

Particularly Helpful Experience

• Experience with real-world data and evidence, including claims, electronic health records, laboratory, pharmacy, registry, or clinical research data.
• Hands-on experience implementing or working with OMOP or another healthcare common data model.
• Familiarity with clinical and administrative vocabularies such as ICD-10, SNOMED CT, LOINC, RxNorm, CPT, or NDC.
• Experience with data-quality frameworks, source-to-target mapping governance, terminology management, or metadata and lineage platforms.
• Experience with cloud data platforms and technologies such as Snowflake, Databricks, AWS, SQL, or Python.
• Exposure to healthcare privacy, de-identification, expert determination, regulated research environments, or GxP-related workflows.
• Experience partnering deeply with data science, informatics, epidemiology, biostatistics, or data-delivery teams.