Role purpose
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.
Enterprise scope and impact
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.
Operating-design safeguards
Data Architect operates horizontally across the four Build sub-pillars to protect semantic standards, AI-ready criteria and data quality.
Solution Architect operates horizontally to ensure solutions compose the data foundation, AICL and enterprise platform spine consistently.
AI Evaluation & Assurance remains independent of builders and owns the evidence pack for ship/no-ship decisions.
Product & Delivery leadership manages roadmap translation, scrum/programme delivery and capacity transparency across pods.
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.
Key relationships & interfaces
Internal
Head of Business Transformation & Technology — International Commercial Division (direct line); peer for Strategy, Value & Innovation and Adoption & Scale.
The CIO organisation — the platform spine and its milestone plan.
Pillar 1 — one priority queue in, engineering capacity out; pods owned by Pillar 1 product owners and crewed from this pillar.
Pillar 3 — hand-offs into adoption, localisation and the in-market network for everything shipped.
Global Commercial Analytics — data scientists, engineers and project managers crewing into pods via the operating agreement.
Legal & Compliance — privacy engineering dotted in for consent, PII and lawful basis.
External
Data-supply vendors (IQVIA, Komodo, Optum, Veeva) and SI partners (e.g. Accenture, Deloitte) crewing into the pool.
Model and platform vendors through the CIO's model-access framework.
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.
Leadership behaviours (Pfizer values)
Courage — holds one priority queue against pressure from every direction and protects data from losing attention to the agent flagship.
Excellence — one standard across the four layers; evaluation independent of the builders; production quality as a habit.
Equity — one deployable pool serving all markets and functions, not the best-connected ones.
Joy — engineers doing their best work in small, empowered pods, with agents drafting first-pass code and people reviewing, hardening and owning.
Scope & decision rights
Decides technical architecture and standards from raw data to running agent, within the CIO platform guardrails.
Owns delivery capacity allocation and crewing recommendations against the agreed portfolio priority queue, with escalation where demand exceeds capacity or technical risk changes sequencing.
Owns the ship/no-ship evidence pack through independent evaluation; Legal & Compliance clears on that evidence.
Owns data-supply and Systems Integrator vendor selection and contracts, with Procurement.
Escalates to the Head of Business Transformation & Technology — International Commercial Division where priority conflicts exceed the plan of record.
Experience & qualifications
15+ years across data engineering and AI/ML or software delivery, with senior leadership of both data and engineering organisations — the rare end-to-end profile this role deliberately demands.
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.
Purpose
Breakthroughs that change patients' lives... 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.
Digital Transformation Strategy
One bold way we are achieving our purpose is through our company wide digital transformation strategy. We are leading the way in adopting new data, modelling and automated solutions to further digitize and accelerate drug discovery and development with the aim of enhancing health outcomes and the patient experience.
Flexibility
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. Let’s start the conversation!
Equal Employment Opportunity
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. Together, we continue to build a culture that encourages, supports and empowers our employees.
DisAbility Confident
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. Our mission is unleashing the power of our people, especially those with unique superpowers. Your journey with Pfizer starts here!
To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.
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