Deputy Director, Data & AI - Data Architect

Role Purpose

The Deputy Director, Data Governance, Analytics & Engineering will play a key leadership role in strengthening the organisation’s data foundations and enabling data-driven decision-making. The role will lead the implementation and operationalisation of data governance, while also driving data analytics and selected data engineering activities to ensure that governed data is accessible, trusted and usable for reporting, analytics, automation and AI use cases.

This role requires a leader who can bridge governance, business requirements and technical implementation, and translate data governance from policy into practical ways of working across the organisation

Key Responsibilities1. Data Governance and Stewardship, Primary Focus

• Drive the implementation and operationalisation of the organisation's Data Governance Framework.

• Establish and continuously improve standards for data ownership, stewardship, data quality, metadata, classification, access and lifecycle management.

• Work with business and technology teams to identify critical data domains, data assets and data owners.

• Establish measurable data quality rules and monitor data quality issues through dashboards and metrics.

• Drive the Data Stewardship programme, including onboarding, enablement and engagement of Data Owners and Data Stewards.

• Ensure governance requirements are embedded into data platforms, analytics solutions and AI use cases.

• Support appropriate controls for data access, sharing, reuse and protection.

• Develop practical governance processes that balance control, usability and speed to value.

2. Data Analytics and Business Insights

• Partner with business teams to translate business questions and operational challenges into data and analytics requirements.

• Lead development of dashboards, management reporting and analytical solutions.

• Identify opportunities to replace manual reporting and data compilation with automated, data-driven solutions.

• Perform exploratory and diagnostic analysis to identify trends, patterns, anomalies and business insights.

• Define appropriate KPIs and metrics together with business stakeholders.

• Promote self-service analytics while ensuring appropriate governance and access controls.

• Support analytics and AI use cases by ensuring that underlying data is sufficiently understood, governed and fit for purpose.

3. Data Engineering and Data Enablement

• Work with data engineering and platform teams to design and implement reliable data pipelines and integrations.

• Support ingestion, transformation and modelling of structured and selected unstructured data.

• Develop or oversee data transformations required to make source data analytics-ready.

• Support data modelling, mapping, lineage and documentation across source systems and enterprise data platforms.

• Identify opportunities to automate data ingestion, transformation, quality validation and reporting processes.

• Ensure data engineering solutions incorporate governance, security, quality and metadata requirements from the outset.

• Collaborate with technical teams on scalable data architecture and reusable data products.

4. Data Quality and Trusted Data

• Establish data quality measures covering areas such as completeness, accuracy, consistency, validity and timeliness.

• Work with Data Owners and Data Stewards to identify root causes and resolve recurring data quality issues.

• Establish clear accountability for remediation.

• Develop dashboards and metrics to provide visibility of data quality and governance adoption.

• Help establish trusted and reusable datasets for enterprise analytics and AI consumption.

5. Stakeholder and Team Leadership

• Engage senior business, technology and data stakeholders to drive adoption of data governance and analytics practices.

• Facilitate discussions across business and technical teams to resolve data ownership, quality, definition and access issues.

• Provide leadership and coaching to data governance, analytics and engineering resources.

• Manage selected vendors and delivery partners where required.

• Build a strong data culture by making governance practical, understandable and relevant to business outcomes.

Job Requirements

• Degree in Computer Science, Information Systems, Data Science, Engineering, Business Analytics or a related discipline.

• Strong experience in data governance, data management, analytics or data engineering, preferably across large or complex organisations.

• Demonstrated experience implementing data governance practices, rather than only developing policies and frameworks.

• Good understanding of data quality, metadata, data lineage, data classification, access management and stewardship.

• Experience with data analytics, BI and dashboarding platforms.

• Working knowledge of data engineering concepts including ETL/ELT, APIs, data pipelines, data modelling and cloud data platforms.

• Familiarity with SQL and modern analytical/data platforms is highly desirable.

• Experience working across business and technology stakeholders and translating business requirements into practical data solutions.

• Strong communication, stakeholder-management and problem-solving skills.

Experience with some of the following would be advantageous:

• SQL and Python

• Power BI or equivalent BI/visualisation platforms

• Snowflake or similar cloud data platforms

• ETL/ELT and data integration tools such as Fivetran

• Data cataloguing, metadata and governance platforms

• Cloud platforms and enterprise data architectures

• Data quality and observability tools

• APIs and system integration

• AI/ML data preparation and governance

What Success Looks Like

The successful candidate will help move the organisation from governance on paper to governance embedded in how data is actually managed and used.

Key outcomes would include:

• Clearly identified and accountable Data Owners and Data Stewards.

• Improved quality and trust in critical enterprise datasets.

• Increased automation of manual data preparation and reporting.

• Greater availability of governed, reusable data for analytics and AI.

• Reduced time spent discovering, reconciling and validating data.

• Governance and controls embedded into data pipelines and analytics solutions.

• Improved adoption of enterprise data and analytics capabilities.