We'rebuilding a connected, end-to-endEnterprise AIengine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain.Success depends on being exceptional connectors:you'llactivelyleverageexisting capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real.

TheDirector, Data Automationdefines and delivers the enterprise automation agenda that reimaginesend‑to‑enddata processes. The role connects strategy,standardsand technology to scalable solutions-linking automation to governance and controls, building reusable patterns, and deploying "AI for data" to improve quality,speedand assurance. Operating within Enterprise Data Programmes, this role leads delivery for the Data Automation pillar in close partnership with Data Project Leadership and Data Change Management. The role also ensures alignment with the evolvingDataOpsAutomation Strategy by contributing delivery insights, patterns andnon‑functionalrequirements that enable CI/CD for data at scale.

Scope of accountability:

You will lead an integrated automation function focused on the Data Automation pillar:

• Automation strategy: Translate enterprise priorities into a practical automation roadmap that targetshigh‑valueopportunities across ingestion, curation, quality, metadata, lineage,accessand compliance workflows.

• Automation marketplace: Lead a catalogue of reusable automation patterns,componentsand tools; govern standards and drive reuse across domains.

• Compliance by code: Link automation to governance/controls (privacy, security,GxPwhere applicable) throughpolicy‑as‑codeand continuous assurance.

• "AI for data": Define and scaleAI‑assistedautomation (e.g., schema mapping, entity resolution, metadata extraction, anomaly detection, documentation generation).

• Technology requirements: Define technical requirements and reference architectures for orchestration, eventing, agents and, whereappropriate, RPA; integrate with enterprise data platforms, MDM,catalog,lineageand monitoring.

• Delivery partnership: Partner with AI‑EPIC and domain teams to design andco‑runpilots; transition successful automations to scaled operations with platform teams.

• Foundational automation practices: Embed Automated Data Quality & Validation, Data Pipeline Orchestration & Workflow Management, and CI/CD for data into delivery, in alignment with the broaderDataOpsAutomation Strategy.

Key accountabilities:

Automation strategy and value targeting:

• Develop and present directional proposals and arguments for priority automation initiatives; articulate value hypotheses, success metrics, resourceneedsand achievements for go/no‑godecisions.

• Maintain an automation opportunity map and multi-year roadmap with clear annual goals and achievements; align with Enterprise Data Programmes strategy and platform/domain roadmaps.

• Prioritise based on outcome potential, riskreductionand reuse; ensure portfolio balance across capabilities and domains.

Marketplace,patternsand standards:

• Establish andoperatethe automation marketplace/catalogue; define contribution and reuse processes,versioningand lifecycle management.

• Author and govern automation standards, codingguidelinesand quality gates; ensure patterns integrate with enterprise policies and platform conventions.

• Measure and report reuse rates, patternadoptionandtime‑to‑valueimprovements.

Compliance by code and continuous assurance:

• Implementpolicy‑as‑codefor privacy,securityandGxP(where applicable); embed automated controls, evidence bring together andaudit‑readinessinto pipelines and workflows.

• Define andoperatemonitoring and alerting for automated processes, including SLAs/SLOs, failure handling,rollbackand resiliency patterns.

AI for data:

• Evaluate and select AI techniques and tooling for data automation use cases (e.g., schema/ontology alignment, data quality anomaly detection, PII detection,lineageand metadata enrichment).

• Set evaluation criteria for model performance,human‑in‑the‑loopthresholds and risk controls; guide pilots from POC to scalable,cost‑effectiveoperations.

Technology and reference architecture:

• Definenon‑functionalrequirements (security, scalability, reliability, observability, cost) and reference architectures for automation components.

• Ensure tight integration with enterprise data platforms,catalog/metadata, lineage,MDMand monitoring;maintaincompatibility with enterprise standards.

• Specify and embed foundational capabilities: Automated Data Quality & Validation (rules, anomaly detection, test harnesses), Data Pipeline Orchestration & Workflow Management (scheduling, eventing, dependency management), and CI/CD for data (versioning, automated testing, deployment and rollback pipelines), contributing to and aligning with theDataOpsAutomation Strategy.

• Oversee vendor/partnerselectionwhereappropriateand manage performance against commercial and quality commitments.

Delivery andscale‑up:

• Co‑designend‑to‑endautomated processes with AI‑EPIC and domain teams; build pilot charters with clear success criteria and exit gates.

• Run pilots and transition to production with platform teams, ensuring support models, runbooks and continuous improvement loops are in place.

• Track benefits (cycle‑timereduction, quality/compliance uplift, cost avoidance/productivity) andcourse‑correctdelivery plans when needed.

Partnerships and governance:

• Partner with Data Project Leadership to align automation milestones with programme stage gates and dependency plans.

• Coordinate with Data Change Management to embed new automation in ways of working,trainingand communications; ensure adoption and behaviour change are sustained.

• Participate in (and, whereappropriate, chair) automation design and risk reviews;maintaintransparent decisions and artefacts for audit and governance forums.

• Collaborate with platform engineering andDataOpsleaders to ensure patterns, pipelines and controls align with the enterpriseDataOpsAutomation Strategy and CI/CD practices.

Essential skills and experience:

• Degree in a scientific,technicalor business discipline, or equivalent experience.

• Proven leadership delivering data automation at scale in a global, matrixed environment with measurable improvements in quality,speedand/or compliance.

• Hands-onexpertiseacross the data lifecycle (ingestion, curation, automated data quality/validation, metadata/lineage, access, controls) and integration with enterprise data platforms.

• Experience defining reference architectures andnon‑functionalrequirements; ability to evaluate/select enabling technologies and manage vendors.

• Working knowledge of privacy/security controls and "compliance by code";evidence of embedding automated controls and continuous assurance.

• Practical experience with "AI for data" automation andhuman‑in‑the‑loopoperating models; able to translate technical detail into business outcomes.

• Demonstrable implementation of data pipeline orchestration/workflow management and CI/CD for data (versioning, automated tests,deploymentand rollback).

• Strong stakeholder management and collaboration across R&D, IT, platform/DataOpsand governance teams; clear communicator with concise decision artefacts.

Desirable:

• Experience in pharmaceutical R&D or other highly regulated industries.

• Knowledge of MDM, datacataloging/lineage, metadatastandardsand data quality frameworks.

• Familiarity with orchestration and eventing platforms,agent‑basedautomation, and RPA whereappropriate.

• Experience with change enablement and training to drive adoption of automated processes.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challengeperceptions.That'swhy we work, on average, a minimum of three days per week from the office. But thatdoesn'tmeanwe'renot flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

At AstraZeneca, we are driven by a shared purpose to make a difference in patients' lives through innovation and collaboration. Our dynamic environment encourages continuous learning and growth as we explorenew technologiesand challenge conventional approaches. By partnering across functions andleveragingour data capabilities, we empower our teams to achieve remarkable outcomes. Join us as we shape the future of healthcare and contribute to AstraZeneca's mission of delivering life-changing medicines.

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