Data Product Engineer

Join a global Contract Resource Organization (CRO) where innovation meets purpose. At Worldwide Clinical Trials, we're driven by a shared commitment to improving lives through high-quality clinical research. Our teams are empowered to think creatively, take ownership, and deliver meaningful work that helps bring life-changing therapies to patients around the world.  With more than 4,000 professionals across the globe, we foster a collaborative and inclusive environment where diverse perspectives are valued, growth is encouraged, and every contribution makes an impact. Whether you're building your career or bringing years of experience, you'll have the opportunity to learn, grow, and help shape the future of clinical research.  SUMMARY: Data Product Engineer will lead the technical evolution of our data assets into high-value, AI-ready products. In this role, you will merge the operational rigor of DataOps with a product-centric mindset, ensuring that our data is discoverable, highly governed, and optimized for Generative AI and Agentic AI workflows. Data Product Engineer will be responsible for the production readiness of our data, including governance and master data management, ensuring that data products supporting data intelligence, analytics, Generative AI, and Agentic AI are governed, reliable, and scalable, while also owning the end-to-end lifecycle from ingestion through master data management to secure consumption via Unity Catalog. RESPONSIBILITIES: Tasks may include but are not limited to: Data Governance & Master Data Management (MDM) Lead the technical implementation of Master Data Management (MDM) solutions to establish the "Golden Record" for clinical trials, ensuring consistency across disparate data sources. Enforce Data Governance policies through automated "policy-as-code" within the data product lifecycle, ensuring compliance with global clinical regulations. Utilize Databricks Unity Catalog to provide centralized access control, rigorous auditing, and end-to-end data lineage across the entire Azure tenant. Collaborate with data stewards to automate the identification and remediation of sensitive data (PII/PHI) within the data lakehouse. Data Product Engineering & AI Readiness Treat data as a formal product by defining and maintaining SLAs, SLOs, and SLIs for data quality and availability across the clinical enterprise. Engineer API-based integrations that allow data products to be seamlessly consumed by internal applications, LLMs, and external partners. Ensuring high-quality metadata and are including in data products and available for Generative AI applications. DataOps Mastery (Data Kitchen & IBM Best Practices) Architect the Data Kitchen "Innovation Pipeline" to allow for the rapid, automated deployment of new data features without compromising production stability.