1 to 25 of 58 Metadata Jobs in the Thames Valley

Databricks Certified, AWS/Azure/GCP Data Engineering certifications) Experience with DataOps, CI/CD practices, and infrastructure-as-code Knowledge of data governance, data quality frameworks, and metadata management Benefits: Hybrid/remote working environment, allowing you a flexible work-life balance to thrive both in the office and from the comfort of your home Competitive compensation package More ❯

knowledge of SQL, Python, and relevant data engineering frameworks (e.g. Databricks, Synapse, Snowflake, Redshift). Experience designing data platforms leveraging PaaS and SaaS solutions. Solid understanding of information governance, metadata management, and master data management principles. Experience leading data design across full project lifecycles (Discovery, Alpha, Beta, Live). Strong understanding of API design, event-driven architecture, and microservices principles. More ❯

and data governance Implement data quality monitoring and alerting processes. Work with data governance teams to ensure compliance with data governance policies and standards. Implement data lineage tracking and metadata management processes. Collaboration & Communication: Collaborate closely with data scientists, economists, and other technical teams to understand data requirements and translate them into technical solutions. Communicate technical concepts effectively to both More ❯

non-technical stakeholders. Nice to Have: • Experience in energy or financial trading environments. • Familiarity with cloud-based data platforms (e.g. Databricks, Snowflake). • Knowledge of data governance, lineage, and metadata management. • Exposure to data cataloguing tools and data quality frameworks. More ❯

vendor evaluation, implementation, and adoption across the enterprise. Establish and maintain reference data management frameworks and governance processes. Develop and execute data strategies, including future state architecture, data governance, metadata management, data privacy, reference data management, and data integration. Lead the design and review of data architectures for both operational platforms (APIs, event streams, transactional systems) and analytical platforms (data More ❯

to collaborate across teams. Preferred Qualifications Experience with Databricks Unity Catalog and Delta Live Tables. Familiarity with streaming frameworks (Structured Streaming, Kafka, etc.). Background in data observability and metadata management. Exposure to machine learning pipelines or MLflow within Databricks. Knowledge of infrastructure as code (IaC) using Terraform or similar tools. To apply for this role please email across your More ❯

e.g., Git, Azure DevOps). Desirable Exposure to Python or Scala for data engineering tasks. Knowledge of Power BI or other visualisation tools. Experience with data governance frameworks and metadata management. If you’re interested, get in touch ASAP with a copy of your most recent and up-to-date CV and email me at b.forbes@tenthrevolution.com or you can More ❯

/or Scala. Expertise in ETL/ELT processes, data warehousing, and data mesh architectures. Familiarity with AI/ML concepts and their application in data analytics. Experience with metadata management, data lineage tracking, and data cataloguing. Knowledge of serverless data processing, event-driven architectures, and modern data stacks. More ❯

that enable scalability and cross-business alignment. Design, build, and optimise data platforms leveraging cloud-native technologies (AWS, Azure, or GCP). Integrate key data management capabilities, including governance, metadata, privacy, and cataloguing. Act as a trusted advisor, translating complex technical concepts into clear business outcomes. Required Skills and Experience 10+ years of professional experience in data architecture, engineering, or More ❯

glossaries, and implement robust data quality frameworks Identify and manage internal and external data sources, ensuring strong access and sharing controls. AI & Technology Enablement Build tools and processes for metadata management, data quality, and data sharing. Leverage AI and automation tools to improve data governance capabilities. Use Python, SQL, PySpark, Power BI, and related tools for data processing and visualization. More ❯

reporting and analytics needs into efficient, well-governed data models. Your leadership will be key in identifying gaps, driving improvements, and implementing best practices in dimensional modelling, normalization, and metadata management. A strong understanding of the Insurance domain—including Specialized Insurance, London Market operations, and Regulatory Data—is essential. You will apply this domain expertise to ensure that data models More ❯

non-technical stakeholders. Expertise in data modelling to at least 3NF, database structures, and data mining. Proficiency in Kimball methodology and Data Vaults . Strong understanding of database fundamentals, metadata-driven ingestion, and transformation pipelines. Experience with Master Data Management (MDM) , data cataloguing, and managing data lineage. Familiarity with Azure Cloud Services , including DataBricks, Azure Data Factory, Azure SQL, CosmosDB More ❯

integration patterns, ETL/ELT processes, and data pipeline orchestration. Experience with AWS data platforms and their respective data services. Solid understanding of data governance principles, including data quality, metadata management, and access control. Familiarity with big data technologies (e.g., Spark, Hadoop) and distributed computing. Proficiency in SQL and at least one programming language (e.g., Python, Java) 6 Month Contract More ❯

squad Product Manager and Scrum Master to increase engineering ways of working, leading to higher quality/faster delivery. Data Management and Governance Practice Data Lifecycle Management through Global Metadata and Access Control Management. Ensure all data models and assets have Data Quality Management standards implemented. Design all new data models in collaboration with Data Domain Leads and Architects. Partner More ❯

user needs when designing deliverables. Experience in designing technology solutions with complex end-to-end data flows. Experience in implementing data governance, including data cataloging, data lineage tracking, and metadata management to ensure data accuracy, accessibility, and compliance. Preferred: Experience with Databricks Understanding of how data platforms interact with marketing and customer engagement platforms. Knowledge of service-oriented architecture, including More ❯

Streams & Tasks, and performance optimization Integration with AWS services and orchestration tools Expertise in data integration patterns, ETL/ELT, and data pipeline orchestration Experience with data quality frameworks, metadata management, and data lineage Hands-on experience with machine learning pipelines and generative AI engineering Familiarity with DevOps practices, CI/CD for data workflows, and infrastructure-as-code (e.g. More ❯

/LODs, and voxel/point-cloud conversions. Implement labeling and QA workflows, manage external annotation vendors, and automate quality checks to ensure scalable, reliable datasets. Define schemas and metadata for digital assets, maintaining catalogues, lineage, and versioning to ensure reproducibility and governance. Enforce data quality, drift detection, and compliance (GDPR/COPPA), including watermarking and traceability to safeguard IP More ❯

BI Publisher, or Tableau for representing data profiling results. Scripting/Automation: Ability to automate DQ checks using Shell, Python, or other scripting tools is a plus. Understanding of Metadata Management Concepts: Helps in building reusable and sustainable DQ frameworks across different domains. Languages: English: C1 Advanced More ❯

and analytics. Define and implement data operating models, target architectures, and transformation roadmaps. Establish data governance frameworks (e.g., DAMA, DCAM) and define stewardship roles and KPIs. Support deployment of metadata management, data lineage, and cataloging tools across platforms. Your Profile: 15+ years of experience in data advisory, data strategy, or data management roles. 8-10 years of experience working within More ❯

aligning with client objectives in financial services. Build relationships at C-suite level by offering data-driven advisory on strategy, transformation roadmaps, and innovation opportunities. Define data governance frameworks, metadata management, and quality controls using a variety of platforms. Inspire and guide cross-functional global teams, ensuring best practices, knowledge sharing, and innovation are embedded in every engagement. Maintain awareness More ❯

design • Experience with cloud data platforms with Microsoft Fabric as the preferred option • Experience with modern data warehousing • Strong data modelling skills across relational and NoSQL • Experience with governance, metadata, master data and integration including ETL ELT and API based approaches The culture is very flat. You will be trusted quickly, supported properly and given a clear path for progression. More ❯

platforms Salesforce implementation desired but not necessary OLAP OLTP modelling experience is a must Familiarity with Orbus iServer Infinity is a plus Experience with Talend Data Catalog or similar metadata tools is advantageous Awareness of CICD pipelines for data solutions is a plus Familiarity with Kanban methodology and agile delivery environments Comfortable collaborating closely with architects engineers product owners Prefer More ❯

Expertise in data governance, BI/MI, and CRM systems, with a proven ability to align them to SDR requirements. Advanced knowledge of data quality frameworks, master data management, metadata, and reference data hierarchies. Proficiency in data validation, cleansing technologies, and lineage tracking. Familiarity with ETL/integration pipelines and validation of outputs for regulatory purposes. Excellent written and verbal More ❯

or performance bottlenecks—and apply robust fixes. Document resolutions to strengthen system resilience. 4. Design, Build, & Govern Data Products Architect, construct, and maintain reusable data products, embedding clean datasets, metadata, governance policies, and clearly defined data contracts. Ensure compliance with FAIR principles—data being Findable, Accessible, Interoperable, and Reusable—and enforce robust access controls in collaboration with governance stakeholders. 5. More ❯

or performance bottlenecks—and apply robust fixes. Document resolutions to strengthen system resilience. 4. Design, Build, & Govern Data Products Architect, construct, and maintain reusable data products, embedding clean datasets, metadata, governance policies, and clearly defined data contracts. Ensure compliance with FAIR principles—data being Findable, Accessible, Interoperable, and Reusable—and enforce robust access controls in collaboration with governance stakeholders. 5. More ❯