Job Opportunities API

A leading global pharmaceutical company is seeking a Director, Data & Analytics to take ownership of the strategy, architecture, delivery, and operational performance of its Data & Analytics function. This is a hands-on leadership role overseeing the enterprise data platform, the data products the business relies on, and the team behind them, setting technical direction, delivery standards, and investment priorities while staying personally engaged in key architecture and design decisions. The ideal candidate combines strong technical credibility with senior leadership experience, and will be directly accountable for making the platform AI-ready, ensuring data is governed, traceable, and model-grade to support regulated AI deployment. Responsibilities: 1. Platform Architecture & Engineering Leadership: Own the enterprise data platform, Azure Data Lake Storage Gen2, Databricks lakehouse (Medallion architecture), Power BI, and Unity Catalog, ensuring it is architecturally sound, standardised, reliable, and engineered to scale Set and enforce platform engineering standards: ingestion patterns, transformation conventions, Medallion layer contracts, data quality gates, Bronze-to-Gold promotion criteria, and the CI/CD framework that delivers all of it Own the Unity Catalog governance model, RBAC, lineage, business glossary, and metric definitions, as the platform-enforced foundation for trusted data Drive the platform roadmap from current state to target, sequencing technical debt remediation, new capability build-out, and platform readiness for downstream AI and analytics demand Personally lead design reviews and architecture decisions for the platform, engaging directly with the engineering team on complex technical problems where senior technical judgment is required Own data platform observability and operational excellence, pipeline reliability, SLA adherence, incident response, and data quality monitoring 2. Delivery & Data Products: Run the D&A delivery programme, from source ingestion and pipeline engineering through to analytics, semantic layer, and data product delivery for business functions Set and enforce delivery standards: sprint cadence, code review, documentation, testing, validation, and release management practices that govern all team output Define and own data SLAs to the business, pipeline refresh frequency, availability, and incident response commitments, and ensure delivery is held against them Own the data integration roadmap, prioritising ingestion of new source systems into the lake in alignment with business demand and platform readiness Manage complex integration challenges across source systems, engaging directly on technical constraints, working with upstream owners, and designing solutions that align data latency and refresh frequency with business needs Own the portfolio of data products, datasets, semantic models, and analytics deliverables, ensuring each is documented, well-understood, and fit for the business question it answers Define and own the standard for what a Gold-layer data product must satisfy to be AI-ready, distinguishing analytics-grade from model-training-grade, with explicit criteria covering completeness, label integrity, statistical consistency, and lineage traceability 3. Data Governance & Quality: Design and operate the data governance operating model, data ownership, stewardship, quality standards, business glossary, and metric definitions Embed data quality gates into Medallion layer promotion, making quality a precondition of Bronze-to-Gold progression, with measurable thresholds and clear remediation paths Own data lineage visibility across the platform so business users can trace the provenance of every number they rely on, end-to-end from source to dashboard Lead the technical evaluation, selection, and implementation of enterprise data catalogue tooling, including integration with Unity Catalog and the platf