Head of Data Platform

Target RWE•Research Triangle Park, NC

13d•Hybrid

About The Position

At Target RWE, our mission is driven by a deep commitment to people, whether it’s the patients we serve, the partners we collaborate with, or the dedicated team members who power our work. As a purpose-driven organization, we leverage real-world data to advance clinical research and inform better healthcare decisions. We foster a collaborative environment where every voice is heard, every idea has impact, and every contribution helps improve lives. If you're seeking a place where your work truly matters, join us to advance science. Overview The Healthcare Data Platform Product Manager will oversee Target RWE’s end-to-end data infrastructure, including data ingestion pipelines, transformation tools, and data delivery to clients or inclusion in a variety of downstream use cases, including prospective clinical trials. They will also develop and optimize the relevant workflows for use by health systems and internal partners to maximize efficiency and high-quality execution. This role defines data ingestion requirements, identifies opportunities to improve data ingestion and data exchange efficiency, plans tooling for scalable data curation, supports dashboarding and data observability, and contributes to data mapping. To do so, the role requires a deep understanding of healthcare data, EHR documentation patterns, ingestion processes, and operational workflows.

Requirements

Strong experience with healthcare data, including EHR and other patient data, and third-party data.
7-10+ years of experience leading scoped product initiatives or owning functional areas, including defining value, workflows, and prioritization.
Strong ability to translate technical concepts (e.g., ingestion workflows, NLP models, mapping rules) into clear, actionable product requirements.
Ability to define and translate operational or data requirements into workflows, functional requirements, or actionable specifications.
Familiarity with AI/ML concepts (e.g., LLMs, SLMs, NLP pipelines, embeddings) and ability to translate model capabilities into product requirements.
Strong skills in building operational dashboards.
Experience improving workflows or operational processes involving healthcare systems.
Demonstrated ability to work collaboratively in multi-functional environments and to lead teams in decision-making.
Excellent communication and requirements-definition ability.

Nice To Haves

Familiarity with data mapping and terminology standards (LOINC, RxNorm, SNOMED) preferred.
Clinical or informatics background is preferred.

Responsibilities

Work with business stakeholders to define what data is needed, in what format, with what cadence, and under what operational constraints.
Create clear ingestion requirements for different data sources.
Translate data documentation patterns and needs into requirements for Engineering.
Ensure ingestion requirements align with site workflows and are not burdensome to implement.
Contribute to the mapping of data elements, ensuring mappings align with standards, and consulting with experts as needed.
Lead the expansion of the data infrastructure to support all data needs, including EHR, other ancillary systems data, or third-party data.
Ensure the data platform can support data reuse in a variety of downstream use cases, including prospective clinical trials, including metadata, data permissioning, and auditability.
Evaluate emerging technologies and propose innovative approaches to make data ingestion more efficient and timely, bringing forward new methods and tools that enhance the data platform and enable novel workflows.
Define requirements for the use of AI technologies, including LLMs, SLMs, NLP models, and other machine-learning approaches, to enhance feature extraction and automation within the ingestion and data processing pipelines.
Collaborate with internal teams and external partners to align processes and tools with Target’s ingestion, mapping, and data readiness requirements, and evaluate alternative approaches to enhance the data platform continuously.
Build and maintain dashboards that monitor key KPIs: data arrival, refresh cadence, pipeline “health”, and data quality. Flag issues proactively and coordinate with Product, QS, and Engineering to address them.