Data Quality & Governance Specialist in Manchester

Requirements:

• 3–5 years in data management, data governance, data quality, or related roles (e.g. data analyst/engineer with strong DQ/governance focus)
• Strong SQL experience writing & managing complex logic with at least one scripting language (e.g. Python, R)
• Proficient profiling data or performing exploratory data analysis & data visualisation tools (e.g. Tableau, Power BI)
• Experience working on Data Governance, lifecycle management processes with working knowledge of data catalog, lineage, and DQ - using tools like Collibra, Monte Carlo (or equivalent)
• Hands-on experience implementing DQ rules, dimensions, thresholds/SLOs, and monitoring/reporting
• Experience with master & metadata management, document/content lifecycle and classification, and data risk mitigation
• Ability to use lineage and metadata for tracing issues and driving root cause analysis
• Experience in ethical data handling, responsible AI, and applying compliance requirements (e.g. GDPR and related regulations) to data and DQ initiatives
• Proven track record leading DQ or governance implementations, including planning, stakeholder management, and progress/risk communications

What the job involves:

• As a Data Manager II, you are responsible for establishing, overseeing, and ensuring adherence to policies, practices, and controls for data management across the company data estate, with a specific focus on data governance and data quality.
• You ensure regulatory compliance, ethical data handling and the responsible adoption of AI models and tooling within data initiatives, while designing and operating data quality frameworks that make our data accurate, trusted, and fit for purpose.
• You drive and support change management for data initiatives, managing projects and stakeholders to successful outcomes and acting as a subject-matter expert for data governance and quality across your scope.
• This is a hands-on role where you will work closely with Product Managers, Data Engineers, Data Scientists, and Insights Analysts to convey the why, how, and what of Data Management initiatives.
• The ideal candidate is passionate about data, eager to learn and able to engage with diverse stakeholders.
• You will report to a Senior Data Manager and collaborate with other Data Managers on strategic objectives for data quality, governance, metadata management and regulatory compliance.
• Implement Data & AI Governance standards, and playbooks within your scope, aligned with enterprise frameworks and regulatory/audit requirements.
• Manage data risks & controls along key data flows to meet regulatory, compliance, risk, privacy and security by design expectations.
• Design and operate Data Quality dimensions, thresholds/SLOs, rules and scoring for priority assets and metrics.
• Monitor and report on data quality, using lineage and metadata for diagnosis, root cause analysis, and pragmatic remediation.
• Embed DQ checks and observability into batch and streaming pipelines, shifting quality left in design and development.
• Apply classification, taxonomies, and metadata management to improve discoverability, protection, and consistent usage of data.
• Define and maintain data lifecycle requirements (creation to deletion) for key assets and keep core documentation (data dictionaries, glossaries, lineage, rule catalogues) accurate and accessible.
• Manage stakeholders across producer and consumer teams, and clearly communicate the business impact of poor data quality and the value of remediation.
• Use SQL and scripting (e.g. Python, R) to profile data, implement/validate DQ checks, and support investigations and prototypes.
• Build and maintain DQ / governance dashboards in tools like Tableau or Power BI to provide clear, actionable views of quality and maturity.
• Use APIs and data management tooling (catalogue, lineage, DQ platforms) to register metadata, automate controls, and integrate governance into workflows.
• Coach and support data managers, stewards, and analysts on governance and DQ topics, and contribute to the wider Data Management community with patterns, documentation, and best practices.