Director, Data Automation

Werebuilding 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:youllactivelyleverageexisting 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 - youll have the platform (and sponsorship) to make it real.

TheDirector Data Automationdefines and delivers the enterprise automation agenda that reimaginesendtoenddata processes. The role connects strategystandardsand technology to scalable solutionslinking automation to governance and controls building reusable patterns and deploying AI for data to improve qualityspeedand 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 andnonfunctionalrequirements 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 targetshighvalueopportunities across ingestion curation quality metadata lineageaccessand compliance workflows.

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

• Compliance by code: Link automation to governance/controls (privacy securityGxPwhere applicable) throughpolicyascodeand continuous assurance.

• AI for data: Define and scaleAIassistedautomation (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 MDMcataloglineageand monitoring.

• Delivery partnership: Partner with AIEPIC and domain teams to design andcorunpilots; 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/nogodecisions.

• 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.

Marketplacepatternsand standards:

• Establish andoperatethe automation marketplace/catalogue; define contribution and reuse processesversioningand lifecycle management.

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

• Measure and report reuse rates patternadoptionandtimetovalueimprovements.

Compliance by code and continuous assurance:

• Implementpolicyascodefor privacysecurityandGxP(where applicable); embed automated controls evidence bring together andauditreadinessinto pipelines and workflows.

• Define andoperatemonitoring and alerting for automated processes including SLAs/SLOs failure handlingrollbackand 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 detectionlineageand metadata enrichment).

• Set evaluation criteria for model performancehumanintheloopthresholds and risk controls; guide pilots from POC to scalablecosteffectiveoperations.

Technology and reference architecture:

• Definenonfunctionalrequirements (security scalability reliability observability cost) and reference architectures for automation components.

• Ensure tight integration with enterprise data platformscatalog/metadata lineageMDMand 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 andscaleup:

• Codesignendtoendautomated processes with AIEPIC 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 (cycletimereduction quality/compliance uplift cost avoidance/productivity) andcoursecorrectdelivery 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 workingtrainingand 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 scientifictechnicalor business discipline or equivalent experience.

• Proven leadership delivering data automation at scale in a global matrixed environment with measurable improvements in qualityspeedand/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 andnonfunctionalrequirements; 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 andhumanintheloopoperating models; able to translate technical detail into business outcomes.

• Demonstrable implementation of data pipeline orchestration/workflow management and CI/CD for data (versioning automated testsdeploymentand 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 platformsagentbasedautomation 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 -person working gives us the platform we need to connect work at pace and challengeperceptions.Thatswhy we work on average a minimum of three days per week from the office. But thatdoesntmeanwerenot 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 AstraZenecas mission of delivering life-changing medicines.

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