Director, Data Automation

We're building a connected, end-to-end Enterprise AI engine - 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'll actively leverage existing 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.  

The Director, Data Automation defines and delivers the enterprise automation agenda that reimagines end‑to‑end data processes. The role connects strategy, standards and technology to scalable solutions—linking automation to governance and controls, building reusable patterns, and deploying “AI for data” to improve quality, speed and 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 evolving DataOps Automation Strategy by contributing delivery insights, patterns and non‑functional requirements 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 targets high‑value opportunities across ingestion, curation, quality, metadata, lineage, access and compliance workflows.  

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

• Compliance by code: Link automation to governance/controls (privacy, security, GxP where applicable) through policy‑as‑code and continuous assurance.  

• “AI for data”: Define and scale AI‑assisted automation (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, where appropriate, RPA; integrate with enterprise data platforms, MDM, catalog, lineage and monitoring.  

• Delivery partnership: Partner with AI‑EPIC and domain teams to design and co‑run pilots; 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 broader DataOps Automation Strategy.  

Key accountabilities:  

Automation strategy and value targeting:  

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

• 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, risk reduction and reuse; ensure portfolio balance across capabilities and domains.  

Marketplace, patterns and standards:  

• Establish and operate the automation marketplace/catalogue; define contribution and reuse processes, versioning and lifecycle management.  

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

• Measure and report reuse rates, pattern adoption and time‑to‑value improvements.  

Compliance by code and continuous assurance:  

• Implement policy‑as‑code for privacy, security and GxP (where applicable); embed automated controls, evidence bring together and audit‑readiness into pipelines and workflows.  

• Define and operate monitoring and alerting for automated processes, including SLAs/SLOs, failure handling, rollback and 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, lineage and metadata enrichment).  

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

Technology and reference architecture:  

• Define non‑functional requirements (security, scalability, reliability, observability, cost) and reference architectures for automation components.  

• Ensure tight integration with enterprise data platforms, catalog/metadata, lineage, MDM and monitoring; maintain compatibility 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 the DataOps Automation Strategy.  

• Oversee vendor/partner selection where appropriate and manage performance against commercial and quality commitments.  

Delivery and scale‑up:  

• Co‑design end‑to‑end automated 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‑time reduction, quality/compliance uplift, cost avoidance/productivity) and course‑correct delivery 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, training and communications; ensure adoption and behaviour change are sustained.  

• Participate in (and, where appropriate, chair) automation design and risk reviews; maintain transparent decisions and artefacts for audit and governance forums.  

• Collaborate with platform engineering and DataOps leaders to ensure patterns, pipelines and controls align with the enterprise DataOps Automation Strategy and CI/CD practices.  

Essential skills and experience:  

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

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

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

• Experience defining reference architectures and non‑functional requirements; 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 and human‑in‑the‑loop operating 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, deployment and rollback).  

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

Desirable:  

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

• Knowledge of MDM, data cataloging/lineage, metadata standards and data quality frameworks.  

• Familiarity with orchestration and eventing platforms, agent‑based automation, and RPA where appropriate.  

• 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 challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not 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 explore new technologies and challenge conventional approaches. By partnering across functions and leveraging our 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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