Vice President, Build — Data, Engineering & AI

The VP, Build — Data, Engineering & AI is the owner of the International Commercial build engine, accountable for turning prioritized demand into trusted, reusable and production-grade data, AI agents, solutions and technical services. The role owns the International commercial data foundation, engineering pool, solution architecture, AI evaluation, AgentOps, production run, technical standards, vendor/Systems Integrator delivery model and CIO-platform integration. Its mandate is to deliver speed without fragmentation, reuse without bureaucracy, and innovation with evidence, reliability, compliance and cost discipline.

This is a Vice President-level enterprise build role spanning the full path from data foundation to production AI. The role operates with senior stakeholders across International Commercial, CIO, CMO, Legal/Compliance, Finance, markets, strategic vendors and SI partners, with accountability for technical strategy, delivery capacity, production quality, run economics and integration with Pfizer’s enterprise platform standards.

Key responsibilities & accountabilities

• The data foundation: Build the trusted commercial data foundation: L1–L4 squads working to one standard (the four data layers, raw to ready-to-use), including reporting & BI and the knowledge & unstructured squad that underpins retrieval quality for the AI Capability Library (AICL).
• Own semantic standards, inheritance rules and AI-ready criteria through the Data Architect; run the Collibra dictionary, master & reference data operations and the governance council.
• Own data quality and observability: shift-left checks, lineage and monitoring built in, not bolted on.
• Run data BAU — incidents, refreshes and access — baselined before any cost reduction is taken.
• Own data products and their owners (global and above-market, including the Customer / CRM and CFC (Customer Facing Colleagues) reporting products), with business data stewards dotted in from markets and functions.
• Manage the data-supply vendor relationships (IQVIA, Komodo, Optum, Veeva, etc..): data and delivery contracts, one door to Procurement.

• Engineering & AI: Own solution architecture: how solutions compose the data, the AICL and the platform, including model-tier selection — mirroring the Data Architect on the data side.
• Run one shared, deployable engineering pool crewed into pods; Systems Integration partners and contractors crew in — never as standing outside teams. Engineering talent is never fragmented, and capacity decisions are transparent against the agreed portfolio priority queue.
• Own product and delivery management: roadmap and translation on the way in; scrum and programme delivery on the way through.
• Build the agent library and standards; deliver the reusable data agents of the AICL and their agent-facing data contracts.
• Run AI evaluation and assurance independently of the builders: evals, red teaming, model-tier proof and the clearance evidence pack.
• Own AgentOps and technical run: agents and classic ML in production, AI cost operations, and solution run & support.

• The CIO seam: Own platform integration and the model gateway — routing each task to the right model, including small language models (SLMs) where quality holds.
• Be the one voice to the CIO on the platform spine; land the commercial data foundation as a certified source on Loom, including the Collibra-to-Loom reconciliation.

• Run & BAU: Keep what is live running well: program support, maintenance, upgrades and data quality across the estate.
• Keep AI running costs visible and managed, feeding the cost signal back to Strategy, Value & Innovation.

Success measures

• Prioritized use cases delivered to production with named owner, evidence pack, run model and sunset criteria.
• Data quality, lineage and observability coverage across priority data products.
• Time from funded demand to production.
• Production reliability, incident rate and support performance.
• AI evaluation coverage, red-team completion and model-tier proof before ship.
• Cost-to-serve, model-routing savings and reuse of AICL / agent patterns across markets.

Experience & qualifications

• 15+ years across data engineering and AI/ML or software delivery, with senior leadership of both data and engineering organisations.
• A track record of building governed data platforms at enterprise scale (semantic layers, data products, quality and observability) and of shipping AI or agentic solutions into production.
• Experience running large engineering organisations (~50–100 including partners) as a flexible pool — pods, squads, deployable capacity — rather than fixed project teams.
• Vendor and SI management at scale, including data and delivery contracts.
• Prior senior leadership (Senior Director / VP level) in biopharma or another regulated data environment strongly preferred; degree in a technical discipline required, advanced degree preferred.
• Proven ability to operate as a senior enterprise leader in a complex global matrix, influencing senior business, technology, legal/compliance, medical, finance, PX and market stakeholders without relying solely on direct authority.

Technical skills & knowledge

• Modern data architecture: layered data platforms (raw to ready-to-use), governed semantics, data contracts, catalogues (e.g. Collibra), lineage and observability.
• Agentic AI: agent platforms and registries, model gateways and model-tier selection including SLMs, evaluation and red-teaming practice, AgentOps and production ML.
• Cloud data and analytics stacks (e.g. Snowflake) and integration with enterprise data fabrics.
• AI cost operations: metering, routing and cost-per-outcome management.
• Engineering leadership: agile delivery at scale, platform engineering, DevOps and run practices.

At Pfizer we are a patient centric company, guided by our four values: courage, joy, equity and excellence. Our breakthrough culture lends itself to our dedication to transforming millions of lives.

We aim to create a trusting, flexible workplace culture which encourages employees to achieve work life harmony, attracts talent and enables everyone to be their best working self.

We believe that a diverse and inclusive workforce is crucial to building a successful business. As an employer, Pfizer is committed to celebrating this, in all its forms – allowing for us to be as diverse as the patients and communities we serve.

We are proud to be a Disability Confident Employer and we encourage you to put your best self forward with the knowledge and trust that we will make any reasonable adjustments necessary to support your application and future career.