Head of Data & BI Platform Engineering

Job description

ROLE SUMMARY The Head of Data & BI Platform Engineering owns Pfizer's enterprise data and business intelligence platform layer. This encompasses the platforms on which Pfizer's structured and semi-structured data is stored, processed, transformed, and visualized, including the organization's core data warehousing and lake platforms (such as Snowflake and Databricks), data integration and transformation tools (such as Alteryx and Mulesoft), and enterprise BI and visualization platforms (such as Tableau, Power BI, and Spotfire). Pfizer's AI & Data Center of Excellence (CoE) exists to accelerate the Enterprise AI Strategy, enabling every function, every builder, and every decision-maker to work faster and with greater precision. At the foundation of that strategy is data: well-structured, governed, and readily accessible data that AI systems, analytics teams, and business leaders can depend on. The data platforms this leader manages are a critical upstream dependency for the majority of AI workloads across Pfizer, making the reliability, performance, and governance of these platforms a direct enabler of the organization's AI ambitions. In this role, you will lead a team of engineers and platform specialists across and will be accountable for delivering these platforms as enterprise services with published SLAs, governed access, and measurable value to the federated AI and analytics teams that depend on them. ROLE SUMMARY This role has direct ownership accountability for the following platform categories: Data Warehousing and Lakehouse Platforms Enterprise data warehouse and lakehouse platforms (e.g., Snowflake, Databricks, Amazon Redshift) including infrastructure, administration, performance tuning, cost governance, and access management. Unified storage and compute environments for structured, semi-structured, and large-scale analytical data workloads across all divisions. Data Integration and Transformation Data pipeline and integration tooling (e.g., Alteryx, dbt, FiveTran, Databricks, or equivalent) including pipeline development standards, orchestration, and operational monitoring. ETL/ELT framework governance, enabling self-service data preparation for analysts while maintaining data quality and lineage standards. Knowledge graph, ontology, and semantic model tooling (e.g., Neo4J, SciBite, Stardog, Neptune, etc.) Business Intelligence and Visualization Platforms Enterprise BI and visualization platforms (e.g., Tableau, Power BI, Spotfire) including platform administration, licensing governance, performance, and user access. Semantic layer and report governance standards, in partnership with the AI Ready Data team, to ensure consistent and trusted metric definitions across the enterprise. Data Catalog and Observability Data catalog tooling and metadata management, enabling data discoverability and lineage tracking across the platform estate. Platform observability and data reliability engineering including SLA monitoring, data quality alerting, and incident response. Key Responsibilities Platform Leadership and Strategy Define and own the Data & BI platform strategy and multi-year roadmap, ensuring platform investments are aligned to the needs of federated AI and analytics teams across Pfizer. Lead, develop, and grow a team of engineers and platform specialists across Platform Operations, Platform Development, and Data Reliability pods. Establish and maintain product-minded ownership of the platform portfolio with roadmap priorities driven by user needs, validated through regular engagement with platform consumers. Own the vendor relationships and licensing strategy for the data and BI platform portfolio, including contract management, performance governance, and technology evaluation. Partner with the Head of AI Ready Data to ensure platform infrastructure and data governance standards are tightly integrated, the platform must serve AI-ready data, not just available data. Platform Engineering and Operations Oversee the design, build, and operation of enterprise data pipelines, warehouse/lakehouse environments, and BI infrastructure at scale. Define and publish platform SLAs covering availability, data freshness, query performance, and pipeline reliability and hold the team accountable to them. Establish data reliability engineering practices including error budgets, on-call processes, incident response, and blameless post-mortems, in coordination with the CoE Platform Reliability / SRE function. Drive platform cost transparency and FinOps practices ensuring compute and storage costs are visible, attributed, and continuously optimized. Ensure all platforms meet Pfizer's enterprise security, privacy, and regulatory compliance requirements, in partnership with the CISO organization and Trusted AI team. Stakeholder Engagement and Adoption Serve as the primary platform interface for federated AI and analytics teams across Pfizer, proactively embedding platform support and enabling self-service access. Partner with Enterprise Architecture and existing Pfizer Digital & Technology data teams to ensure the CoE platform extends and modernizes existing data infrastructure investments rather than duplicating them. Contribute to the AI & Data Guild network, enabling cross-functional awareness of platform capabilities, best practices, and available data assets. Report regularly to the Chief AI and Data Officer on platform health, SLA performance, adoption metrics, and investment priorities. BASIC QUALIFICATIONS Master’s degree in Computer Science, Information Systems, Engineering, or a related discipline; or equivalent years of experience. 15+ years of progressive experience in data engineering, data platform architecture, or enterprise analytics infrastructure, with a minimum of 5 years in a senior engineering leadership role. Demonstrated hands-on experience administering and scaling enterprise data warehousing or lakehouse platforms at scale, including at least one of the following: Snowflake, Databricks, Amazon Redshift, or Azure Synapse. Proven experience managing enterprise BI and visualization platforms (e.g., Tableau, Power BI, or Spotfire), including governance, performance, and adoption programs. Experience with data integration and pipeline tooling (e.g., Alteryx, dbt, Informatica, Fivetran, or equivalent) in a large enterprise context. Proven track record of leading platform teams with an SLA-driven, product-minded operating model with documented outcomes in platform reliability, cost efficiency, and user adoption. Strong command of data catalog, metadata management, data lineage, and data quality frameworks in enterprise environments. Experience establishing platform governance in regulated industries, including data privacy controls, access management, and audit trail requirements. Demonstrated ability to lead through influence in a federated organizational model — driving adoption through value delivery and user experience, not mandate. Exceptional communication skills, with the ability to translate platform strategy and technical trade-offs for both technical teams and executive stakeholders. Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact. PREFERRED QUALIFICATIONS Experience in the biopharmaceutical, life sciences, or healthcare sector, with familiarity with GxP data requirements, HIPAA, and FDA data integrity expectations. Experience supporting AI and machine learning workflows as a data platform consumer understanding of feature engineering pipelines, training data requirements, and model data dependencies. Familiarity with semantic layer tools (e.g., dbt metrics layer, Looker LookML, AtScale) and governed metric management. Experience operating within or alongside a Center of Excellence or shared serv