Recruitment Data Policies DT Lead - Commercial Data Engineering & Data Product

UCB•Atlanta, GA

4d•Hybrid

About The Position

The DT Lead, Commercial Data Engineering & Data Products is responsible for the strategy, delivery, operation, and continuous evolution of U.S. Commercial data engineering and data products on the NextGen platform. This leader combines technical expertise in Azure, Databricks, and modern data engineering with a product-oriented approach to ensure commercial data assets are trusted, reusable, scalable, and aligned with business outcomes. The role owns commercial data products throughout their lifecycle, from design and delivery through adoption, operation, enhancement, and retirement. The position establishes standards for data quality, data contracts, lineage, observability, testing, governance, and operational support. It defines and enables enterprise-aligned semantic models, certified business definitions, and reusable data products for reporting, analytics, machine learning, AI, and future agent-enabled capabilities. The role partners with Commercial stakeholders, Product Owners, Data Management & Governance, Reporting, Advanced Analytics, AI Enablement, Enterprise Architecture, Security, Platform Engineering, and external delivery partners. It leads the Commercial Data Engineering team and delivery partners, oversees platform reliability and operations, and owns the Commercial Data Engineering and Data Product roadmap.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, Data Engineering, or a related field, or equivalent practical experience.
10+ years of progressive data engineering or cloud data platform experience, including technical leadership of complex enterprise delivery and operations.
Prior experience in pharmaceuticals, life sciences, healthcare, or another regulated industry.
Prior experience across commercial data domains such as claims, HCP/customer, payer, patient services, specialty pharmacy, omnichannel, territory, or master data.
Hands-on experience with Azure and Databricks or AWS/Google Cloud, including Python, SQL, Spark, Delta Lake or lakehouse patterns, orchestration, performance tuning, and production troubleshooting.
Experience delivering trusted data products for Power BI/Fabric, advanced analytics, machine learning, RAG, copilots, or agent-enabled applications.
Experience designing scalable ETL/ELT pipelines, metadata-driven ingestion, reusable transformation frameworks, and automated data-quality controls.
Demonstrated data product experience, including product definition, ownership, consumer alignment, data contracts, quality and service measures, roadmap management, adoption, and lifecycle decisions.
Experience with Unity Catalog or comparable governance capabilities, including access controls, metadata, lineage, classification, and environment promotion.
Experience with Git-based development, CI/CD, release controls, automated testing, observability, and incident management.
Working knowledge of AI/ML and GenAI data requirements, including features, RAG or vector data, model and agent integration, telemetry, security, and supportability.
Experience leading engineers and third-party partners and communicating technical risks and tradeoffs to business and senior stakeholders.

Nice To Haves

Experience delivering trusted data products for Power BI/Fabric, advanced analytics, machine learning, RAG, copilots, or agent-enabled applications.

Responsibilities

Own the lifecycle of Commercial data products from onboarding through retirement, including product ownership, consumer needs, data contracts, quality standards, service levels, and roadmaps.
Drive adoption of certified, reusable data products across reporting, analytics, AI, and business teams.
Lead the Commercial Data Engineering team responsible for data onboarding, integration, transformation, publishing, and production support.
Define engineering standards across the NextGen platform, including architecture, data quality, lineage, security, testing, and operational readiness.
Deliver scalable Azure and Databricks solutions using modern engineering practices, automation, and reusable frameworks.
Establish enterprise-aligned semantic models, reusable business definitions, and certified metrics for analytics, reporting, and AI solutions.
Ensure interoperability between Databricks, Fabric, Snowflake, Power BI, and future enterprise platforms.
Serve as technology lead for Commercial data domains, including customer, HCP/HCO, claims, payer, specialty pharmacy, patient services, omnichannel, territory, and master data.
Partner with business stakeholders, Data Management, Data Governance, and external vendors to ensure commercial data assets are trusted, compliant, and fit for purpose.
Oversee onboarding, lifecycle management, quality, and operational support of internal and third-party commercial data sources.
Drive standardization and reuse of commercial data assets and products across Reporting, Analytics, AI, and Commercial Operations teams.
Ensure the reliability, performance, observability, and cost efficiency of Commercial data products and pipelines.
Lead incident, problem, change, and release management while driving automation and continuous improvement.
Establish support models, operational runbooks, service-level expectations, and build-to-run governance.
Enable trusted data products for machine learning, RAG, Databricks Genie, copilots, agents, and future AI capabilities.
Partner with AI and Data Science teams to support feature engineering, retrieval pipelines, model inputs, and operational requirements.
Ensure AI solutions use governed, secure, traceable, and high-quality data assets.
Lead internal teams and delivery partners while building long-term organizational capability.
Own the Commercial Data Engineering and Data Product roadmap, balancing business priorities, platform modernization, reliability, and future AI enablement.