Position Summary Are you a data leader who wants to build and manage enterprise data products on which a major pharmaceutical business bases its commercial decisions - while staying hands-on in the data model, the code standards, and the roadmap? If yes, this Director, Commercial Data Products role could be an ideal opportunity to explore.
This role owns key enterprise data products, including products in the Commercial Activity and Customer & Account domains that people and AI use directly to answer their own questions quickly, consistently, and with confidence. Success in this role requires strong partnership across GSK, including with engineering teams that ingest and prepare data, analytics teams, commercial and field operations, tech partners, and master data and data governance teams.
The role establishes or refines the data model and product roadmap and is accountable for the quality, reliability, documentation, and versioning of the products. Accountability does not stop at the pipeline: the role owns the SLAs, published definitions, and catalog documentation through which products reach their users, and their adoption.
This is a hands-on product owner role, not a request-management role, partnering with an engineering team to build and maintain the products, leading a team of contractors, and setting the engineering bar by example: version-controlled, peer-reviewed, tested, and documented code. Business functions retain ownership of what metrics and rules mean; this role encodes, versions, and certifies them.
Key Responsibilities This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following:
- Own a set of key enterprise data products in the Commercial Activity and Customer & Account domains - for example, Customer Interactions, Sales & Performance, Customer 360 - setting product vision, scope, SLAs, versioning, and the standard against which each is certified, and driving adoption while retiring previous data products.
- Own the product roadmap: gather and prioritize stakeholder requirements, negotiate scope and trade-offs directly with the business, and make firm commitments with clear delivery timelines agreed against published capacity. Represent the domains in regular demand review meetings and publish roadmaps, backlogs, and release notes so consumers can see what is coming and when.
- Own the data models for the products in the domains - conceptual, logical, and physical (Erwin or equivalent) - designing for reuse and for the hard problems in commercial data: effective-dating, data restatements, HCP/HCO hierarchies and affiliations, and consistent keys across masters and products.
- Partner closely with the matrixed engineering team that builds and maintains the products on Databricks, following enterprise platform standards. Own what gets built, in what order, and to what definition of done.
- Champion engineering best practices across everyone who contributes, including contractors - Git-based version control, pull requests with peer review, automated tests alongside models, CI/CD, and everything-as-code - and review designs and code with the depth to be a credible partner to engineers, contributing hands-on when the work demands it.
- Lead a team of contractors supporting the domains - setting priorities, quality standards, and expectations, and ensuring knowledge is documented and retained - while influencing engineering and stewardship partners who contribute to the products without direct reporting lines.
- Own the key source relationships feeding the domains (for example syndicated sales data and CRM data) alongside source data stewards, writing the contents-and-format specification for each source; consume the certified Customer Master as the foundation for 360 products and provide requirements on identity rules, hierarchies, and affiliations.
- Encode, version, and certify business definitions and rules supplied by named business owners - never defining what a metric means, and not certifying without a named owner - and certify each product as AI-ready (permitted use cleared, semantics documented, lineage complete), with catalog entries and documentation good enough for teams and AI agents to self-serve.
- Manage change responsibly: give consumers advance notice, versioned releases, parallel-run periods, and mapping documentation when definitions or structures change.
- Act as the domain authority for consuming teams and report on product health - SLA attainment, time to availability for new sources and upstream changes, internal data integrity, adoption, and impact of data - explaining complex data topics in business terms and influencing senior stakeholders with data.
Why You?
Basic Qualification We are seeking professionals with the following required skills and qualifications to help us achieve our goals:
- Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field, or an equivalent combination of education and experience.
- 10+ years of experience in data engineering, data product management, data architecture, or data management.
- Data engineering experience, including Git and strong coding best practices - branching, pull requests, peer code review, automated testing, and CI/CD - with strong SQL and experience with dbt and/or Python or Spark.
- Experience in data model design - conceptual, logical, and physical - for analytical or enterprise data platforms, including dimensional modeling, effective-dating, and handling historical restatement.
- Experience managing a product roadmap - gathering and prioritizing stakeholder requirements, negotiating scope, making commitments with clear delivery timelines, and delivering against them.
- Experience leading teams - including contractor, contingent, and matrixed teams - with a track record of setting direction, raising quality standards, and delivering through people who do not report to you.
- Experience owning data products or data assets end to end, with defined SLAs, versioning, documentation, and measured adoption.
- Experience with commercial pharma data and ecosystems - for example CRM interactions, syndicated sales and market data, HCP and account data, and field execution use cases.
- Experience with modern cloud data platforms, such as Databricks, Snowflake, and Azure, and with a lakehouse layering approach (raw, enriched, curated).
Preferred Qualification If you have the following characteristics, it would be a plus:
- Master's or other advanced degree in a related field.
- Strong facilitation, communication, and influencing skills, with the ability to negotiate with senior stakeholders and present complex data topics to large audiences.
- Significant experience in a regulated industry such as healthcare, pharmaceuticals, or life sciences.
- Experience with Databricks and Unity Catalog, and with dbt as the authoring layer for transformation logic.
- Experience with Veeva CRM data, IQVIA or other syndicated data, and HCP/HCO affiliations and hierarchies.
- Experience with Customer 360 or similar multi-source customer data products, including consuming a mastered customer domain (for example from Reltio).
- Hands-on experience with Erwin Data Modeler or comparable data modeling tools.
- Experience defining schema contracts, data quality tests, and observability for production pipelines.
- Strong experience with semantic layers, metric governance, data catalogs, and lineage tools, and with preparing data products for AI and agent consumption.
- Experience leading or supporting cloud platform migrations, including dual-running periods.
- Working knowledge of Agile delivery and of healthcare and pharma data privacy requirements, including permitted-use and consent considerations.
Location and Working Model
This role follows a hybrid working model. You will have the flexibility to work both remotely and onsite to connect with your teams and stakeholders.
#GSK-LI
SkillsAgile Methodology, Cloud Platform, Customer 360, Customer Feedback Management, Databricks Platform, Data Governance, Data Visualization, Feature Prioritization, Generative AI, Git, Platform Development, Product Design, Product Development, Product Lifecycle Management (PLM), Product Roadmapping, Python (Programming Language), Structured Query Language (SQL), Usability Testing, User Experience (UX), User Experience (UX) Design, User Needs Analysis, Veeva CRM
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