Description

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, standardized, 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 platform metadata layer.
Embed appropriate data handling, validation, and audit-trail practices for regulated data domains, partnering with Quality, Regulatory, and Legal to ensure compliance requirements are met by design.
Establish data quality measurement as a managed practice, KPIs, dashboards, periodic review, and accountability with data owners.
Establish data lineage and provenance practices that satisfy AI explainability and regulatory auditability requirements, including GxP-compliant audit trails for Quality, Manufacturing, and Regulatory AI use cases.

4. Team leadership & capability development:
Lead, hire, and develop the D&A team, Data Engineers, Analytics Engineers, BI Developers, and Business Analysts, sequencing hires in alignment with the platform roadmap and delivery demand.
Define and evolve the role design, skills profile, and career framework for the team within the broader Data & AI CoE structure.
Set the team's performance culture: clear ownership, high engineering standards, fast feedback, continuous learning, and documentation as a team discipline.
Coach and develop team members directly, identifying high-potential individuals, investing in their technical and leadership growth, and building bench strength across roles.
Manage team capacity and allocation across the platform roadmap and business delivery demand, ensuring focus stays on high-value work aligned to strategic priorities.
Set the engineering craft culture, code review, design review, pairing, and shared technical standards, that lifts the technical quality of all team output.

5. Business partnership & vendor engagement:
Serve as the senior D&A point of contact for business function leadership, translating business data needs into platform and delivery priorities.
Build credibility with business stakeholders through reliable, consistent delivery, data products that are well-understood, well-documented, and match business expectations.
Communicate proactively on platform status, delivery commitments, risks, and trade-offs, ensuring stakeholders have a current view and surfacing issues early.
Represent D&A in cross-functional planning forums, ensuring the data foundation perspective is present in enterprise architecture, application, and AI investment decisions.
Manage operational vendor and partner relationships for the data platform, Databricks, Microsoft Azure, Power BI, and implementation or augmentation partners.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or related discipline.
Master's degree in Data Science, Computer Science, Business Administration, or related field is preferred.
Relevant certifications in cloud data platforms (e.g., Azure Data Engineer, Databricks Certified Data Engineer Professional) are preferred.
Minimum 10 years of progressive experience in data engineering, data platform, or enterprise analytics roles.
Minimum 5 years in a senior leadership role with direct team management accountability, hiring, developing, and performance managing a multi-disciplinary technical team.
Proven hands-on production experience with Databricks (Delta Lake, Unity Catalog) and Azure data services at platform-design and engineering-lead level.
Proven track record building or substantially remediating a cloud-native data platform in a complex, multi-source enterprise environment.
Experience with Medallion/lakehouse architecture patterns in production.
Experience designing data products and platform capabilities for AI/ML consumption, including Feature Store design, training dataset engineering, and ML data lineage is preferred.
Experience working at the interface of a data platform team and an AI/ML team, translating model requirements into data infrastructure specifications and owning the data readiness handoff is preferred.
Experience leading a data governance or catalogue implementation, from design through to business adoption.
Life sciences, pharmaceutical, or other regulated industry experience is preferred.

Skills:

Technical competencies:
Data platform architecture
Data engineering, pipeline design & CDC patterns
Data modelling, transformation & engineering standards
Data governance, quality & catalogue
Analytics & BI delivery
MLOps & ML platform foundations
Delivery management & agile methods

Platform & technical skills:
Deep practical expertise in Azure data services: ADLS Gen2, Azure Data Factory, Azure DevOps.
Hands-on production experience with Databricks, Delta Lake, notebooks, Unity Catalog, MLflow.
Strong understanding of incremental load and CDC patterns across enterprise source systems (SAP, Veeva, SuccessFactors, and similar).
Power BI at the semantic layer level, understanding how the semantic layer should be designed for enterprise scale.
CI/CD for data pipelines, practical implementation at production scale.
Data quality frameworks, profiling, expectation testing, alerting, and remediation workflows.
Working knowledge of modern data governance tooling: Unity Catalog, Collibra, Purview, or DataHub.

Leadership & business skills:
Credible with both technical teams and senior business stakeholders.
Strong delivery discipline, owns commitments, communicates risks early, and sizes work realistically.
Structured thinker, able to take a complex current state and produce a clear, prioritised, sequenced roadmap.
Strong written and verbal communication in English; Arabic proficiency valued.
Cultural intelligence for working effectively across MENA, US, and Europe.