R and DA Automation Lead

Job Summary R&DA Automation Lead Role purpose R&DA Automation Lead

• Function - Reporting & Data Analytics (R&DA)
• Focus - Automation, innovation, standardisation and process efficiency
• Role type - Transformation, solution delivery and change leadership

The opportunity Primary objectives

• Reduce manual and non-value-adding work through automation and AI-enabled solutions.
• Establish common process, data, reporting, documentation and control standards before technology is applied.
• Build a prioritised, transparent and value-led automation pipeline.
• Improve turnaround time, accuracy, consistency, controls and scalability.
• Encourage experimentation, reuse and responsible adoption of emerging capabilities.
• Create sustainable ownership through documentation, training, support models and continuous improvement.

Key responsibilities Automation, standardisation and portfolio leadership

• Define and maintain the R&DA automation, innovation, standardisation and process-efficiency roadmap.
• Translate business and transformation priorities into a sequenced portfolio of initiatives.
• Establish intake, assessment and prioritisation criteria covering value, feasibility, complexity, risk, control impact and scalability.
• Balance quick wins with strategic, reusable capabilities and longer-term platform opportunities.
• Maintain visibility of pipeline, delivery status, dependencies, decisions, benefits and next-wave opportunities.

Opportunity discovery and process efficiency

• Lead discovery sessions with process owners, report owners, SMEs, technology teams and stakeholders.
• Build fact-based baselines covering volumes, effort, cycle time, frequency, manual touchpoints, exceptions, rework, quality issues and controls.
• Map current processes and identify bottlenecks, duplication, low-value activities and unnecessary hand-offs.
• Challenge processes constructively and simplify, standardise or rationalise them before automating.
• Identify opportunities for report consolidation, decommissioning, self-service, re-platforming and improved operating models.

Standardisation and scalable operating practices

• Define and maintain common standards for processes, report structures, naming conventions, templates, business rules, controls, documentation and repositories.
• Identify variations across teams and regions, distinguish justified local requirements from avoidable complexity and drive alignment to agreed standards.
• Create reusable process patterns, automation components, parameter libraries, validation rules, exception categories and support artefacts.
• Establish design and readiness checkpoints so initiatives enter build only when inputs, ownership and standards are sufficiently complete.
• Track adoption of agreed standards and address non-compliance, duplication or unnecessary customisation through governance and stakeholder engagement.

Innovation and AI enablement

• Identify practical use cases where AI, advanced analytics, automation or workflow capabilities can enhance reporting, insights, efficiency and decision support.
• Facilitate ideation, proof-of-concept activity and controlled pilots with clear hypotheses and success measures.
• Promote responsible experimentation, reuse of proven patterns and evidence-based scale-up decisions.
• Monitor emerging capabilities and translate relevant developments into practical R&DA opportunities.
• Build an innovation culture through showcases, communities, reusable assets and knowledge sharing.

Solution design and delivery

• Convert approved opportunities into clear business requirements, process designs, acceptance criteria, delivery plans and ownership models.
• Partner with business, enablement, data and technology teams to assess feasibility and define scalable solutions.
• Address source access, service accounts, environments, licences, security, privacy, compliance, integration, monitoring and support needs.
• Guide solution build and configuration while maintaining traceability to business outcomes and control requirements.
• Ensure standard design patterns, documentation and governance are applied across initiatives.

Testing, deployment and operational readiness

• Oversee unit, system, integration, performance, exception and user-acceptance testing as appropriate.
• Ensure automated outputs are compared with approved baselines and that defects are tracked to closure.
• Confirm deployment readiness, rollback considerations, runbooks, monitoring, alerts, access and support arrangements.
• Lead pilot, go-live, stabilisation and handover activities.
• Embed operational controls, ownership, service expectations and escalation routes.

Benefits, controls and continuous improvement

• Define baselines and measures for effort reduction, cycle time, quality, control effectiveness, adoption and stakeholder experience.
• Track forecast and realised benefits and secure appropriate business acceptance.