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.
Implement IBM DataOps best practices by creating a unified data virtualization and governance layer that drastically reduces data silos.
Automate the "Value Pipeline" using CI/CD for data to ensure that data products are continuously monitored for quality and statistical drift.
Foster a culture of "observability" where automated testing is a required part of the data product (governance, MDM) pipeline.
Perform other duties as assigned. The duties and responsibilities listed above are representative of the nature and level of work assigned and are not necessarily all-inclusive.
OTHER SKILLS AND ABILITIES:
Cloud Infrastructure: Advanced expertise in the Azure ecosystem, including Azure Data Factory, Azure DevOps, and Azure Key Vault.
Databricks Stack: Mastery of Databricks, specifically Unity Catalog for governance and Delta Lake for resilient data lakehouse architecture.
Modern Integration: Strong proficiency in PySpark, SQL, and building/consuming RESTful APIs for data exchange programmatic workflow scheduling using Apache Airflow, Databricks Lakeflow, or Prefect.
REQUIREMENTS:
Experience: 3+ years in master data management, data governance technology, and/or data engineering with at least 2 years focused specifically on architecting DataOps, data governance, and/or master data management frameworks and platforms.
Governance & MDM: Proven experience building MDM frameworks and implementing automated data governance tools in a regulated environment.
Promotion to the next level is not automatic based on years of experience. Personnel are evaluated on proven competency, level of responsibility, and ability to work independently.
Discover the Difference
We love knowing that someone is going to have a better life because of the work we do. Discover the Difference at Worldwide Clinical Trials. Explore additional opportunities on our careers page, visit , or connect with us on LinkedIn.