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.