Data Product Engineer

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

Lead the technical implementation of Master Data Management (MDM) solutions to establish the "Golden Record" for clinical trials; Enforce Data Governance policies through automated "policy-as-code" within the data product lifecycle; 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; Treat data as a formal product by defining and maintaining SLAs, SLOs, and SLIs for data quality and availability; Engineer API-based integrations that allow data products to be consumed by internal applications, LLMs, and external partners; Ensure high-quality metadata is included in data products and available for Generative AI applications; Architect the Data Kitchen "Innovation Pipeline" for 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; Automate the "Value Pipeline" using CI/CD for data, including continuous monitoring for quality and statistical drift; Foster a culture of "observability" where automated testing is a required part of the data product pipeline; Perform other duties as assigned

Requirements

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; Proven experience building MDM frameworks and implementing automated data governance tools in a regulated environment; Advanced expertise in the Azure ecosystem, including Azure Data Factory, Azure DevOps, and Azure Key Vault; Mastery of Databricks, specifically Unity Catalog for governance and Delta Lake; Strong proficiency in PySpark, SQL, and building/consuming RESTful APIs for data exchange