1 to 25 of 48 Permanent Metadata Jobs in London

Governance and Quality Assurance: Embed governance, security, and data quality practices into engineering workflows. Define guardrails and reference implementations for data access control, data lineage, and compliance. Promote consistent metadata management and enforce technical standards to ensure trust in data assets. Stakeholder Engagement: Collaborate with PN D&A leadership, PN product owners, and segment D&A leadership to synchronize and More ❯

Governance and Quality Assurance: Embed governance, security, and data quality practices into engineering workflows. Define guardrails and reference implementations for data access control, data lineage, and compliance. Promote consistent metadata management and enforce technical standards to ensure trust in data assets. Stakeholder Engagement: Collaborate with PN D&A leadership, PN product owners, and segment D&A leadership to synchronize and More ❯

Governance and Quality Assurance: Embed governance, security, and data quality practices into engineering workflows. Define guardrails and reference implementations for data access control, data lineage, and compliance. Promote consistent metadata management and enforce technical standards to ensure trust in data assets. Stakeholder Engagement: Collaborate with PN D&A leadership, PN product owners, and segment D&A leadership to synchronize and More ❯

Governance and Quality Assurance: Embed governance, security, and data quality practices into engineering workflows. Define guardrails and reference implementations for data access control, data lineage, and compliance. Promote consistent metadata management and enforce technical standards to ensure trust in data assets. Stakeholder Engagement: Collaborate with PN D&A leadership, PN product owners, and segment D&A leadership to synchronize and More ❯

Governance and Quality Assurance: Embed governance, security, and data quality practices into engineering workflows. Define guardrails and reference implementations for data access control, data lineage, and compliance. Promote consistent metadata management and enforce technical standards to ensure trust in data assets. Stakeholder Engagement: Collaborate with PN D&A leadership, PN product owners, and segment D&A leadership to synchronize and More ❯

skills. Preferred Qualifications: Cloud certification (Azure Data Engineer, AWS Certified Database Specialty, etc.). Familiarity with NoSQL or NewSQL databases (MongoDB, Cassandra). Experience with data governance, MDM, and metadata management tools. Working knowledge of DevOps tools (Terraform, Git, Jenkins). More ❯

Administer Microsoft Fabric Lakehouse and Azure services Optimise performance across queries, datasets, and pipelines Apply data validation, cleansing, and standardisation rules Document pipeline logic and contribute to business-wide metadata catalogues Collaborate with BI, Change, and Product teams to align data outputs to needs Support groundwork for future data science and machine learning initiatives The successful applicant will be proficient More ❯

implementing cloud data migration and storage patterns on one or more of AWS, GCP and Microsoft Azure Experience implementing and integrating data management platforms for data cataloguing, classification and metadata management. Experience designing and developing data privacy, security and entitlements frameworks for cloud provider ecosystems (AWS, Azure, GCP). Good understanding of cloud networking architecture, operations, automation and cost management. More ❯

etc. Conceptual and Logical design of the Data & Analytics Platform; together with experience of data repository consolidation and end user computing Supporting Data Governance colleagues in areas such as metadata management, data lineage, data audits and general data quality improvement Experience of implementing Master Data Management approaches and tooling Selection of external systems/services with a data component - in More ❯

into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive data in line with GDPR and internal policies. What More ❯

into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive data in line with GDPR and internal policies. What More ❯

into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive data in line with GDPR and internal policies. What More ❯

into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive data in line with GDPR and internal policies. What More ❯

into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive data in line with GDPR and internal policies. What More ❯

into a unified data platform. Cleanse, transform, and model data to support BI tools (e.g., Power BI) and AI/ML use cases. Implement data validation, lineage tracking, and metadata tagging for observability and trust. Collaborate with stakeholders to standardise KPIs and support cross-functional reporting. Ensure secure handling of sensitive data in line with GDPR and internal policies. What More ❯

leading across Product, Engineering, and Data teams. Experience navigating ambiguity and delivering structure to complex, evolving domains. We'd love to see: Experience with data governance, quality frameworks, and metadata management. Understanding of third-party entity data providers and integration strategies. Experience with modern data infrastructure and architecture, including APIs, data pipelines, or LLM-based enrichment solutions. Awareness of emerging More ❯

business capability and process taxonomies, maturity assessments and alignment of gaps to enabling technology solutions Appreciation of and interest in attaining end-to-end data skills e.g., data quality, metadata, data-mesh, data security, privacy & compliance Experience with Enterprise/platform/application (e.g. cloud/SAP)/data architecture Understanding of public and private cloud architectures Knowledge of architecture More ❯

designing and implementing data architectures on AWS platforms. Ability to create and present entity relationship diagrams, logical data models, and high-level architecture diagrams. Familiarity with data modelling standards, metadata management, and data governance. Knowledge of security, privacy, and compliance requirements within UK Government projects. Proficiency with tools like Lucidchart, ER/Studio, Draw.io, or similar for diagramming. Excellent communication More ❯

usage Support analytics and self-service reporting initiatives Skills & Experience Strong experience with GCP, BigQuery, Dataflow Proficiency in Python and Terraform Skilled in ETL/ELT, data modelling, and metadata management Familiarity with CI/CD, DevOps, and Infrastructure-as-Code Knowledge of insurance data and KPIs is advantageous but not essential Benefits: Competitive salary and benefits Discretionary bonus Hybrid More ❯

usage Support analytics and self-service reporting initiatives Skills & Experience Strong experience with GCP, BigQuery, Dataflow Proficiency in Python and Terraform Skilled in ETL/ELT, data modelling, and metadata management Familiarity with CI/CD, DevOps, and Infrastructure-as-Code Knowledge of insurance data and KPIs is advantageous but not essential Benefits: Competitive salary and benefits Discretionary bonus Hybrid More ❯

team model to maximize impact. Partnering with the Executive team to influence strategic decision-making through analytics. Overseeing Data Governance and Data Management, including Data Quality, Self-Serve enablement, Metadata Management, Data Protection, and Privacy. Aligning with the Business Strategy and executing the Data Strategy accordingly. Building influence across the organization and developing key relationships. We seek a candidate who More ❯

and scripting. Hands-on experience with workflow orchestration tools such as Airflow. Strong version control and DevOps exposure (Git, GitHub Actions, Terraform). Familiar with data quality tools and metadata/cataloguing (e.g. Great Expectations, Unity Catalog). Beneficial: MarTech domain knowledge. Notable: This is a hybrid engagement represented by 2 days/week onsite, either in Central London or More ❯

working. With confidence in data integration design, you establish standards, keep them up to date, and ensure adherence to best practices across industry and government. You can identify how metadata repositories support different areas of the organisation and set up robust governance processes to maintain accuracy. Problem-solving is a strong suit, including anticipating challenges, understanding their broader impact, and More ❯

and productivity tools (e.g. Miro, Templafy, etc.) is a plus. Working knowledge of digital workplaces, employee portals, intranet platforms, digital communication tools, and workplace analytics. Understanding of knowledge architecture, metadata, tagging, and content lifecycle management. Basic familiarity with AI and automation trends in the context of knowledge delivery and workplace productivity (e.g., Copilot etc.). Other requirements You possess strong More ❯

assets.4. Builds and deploys communication plans for data consumers so they are aware of data governance standards and available resources for accurate reporting of the data.5. Develops a centralized metadata and classification repository for data assets that can be systematically used with confidence in business applications.6. Fosters communication and knowledge exchange among data governance practitioners through engagement with CoEs7. Operationalizes More ❯