1 to 25 of 52 Permanent Metadata Jobs in Central London

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 ❯

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 ❯

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 ❯

data modelling, governance, and architecture best practices . Excellent leadership, communication, and stakeholder management skills. NICE TO HAVE: Background in data management, engineering, or database administration . Experience with metadata management and master data management (MDM) tools. Understanding of data governance frameworks and compliance standards. Familiarity with machine learning pipelines or AI model integration. A proactive, detail-oriented approach and 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 ❯

In-depth knowledge of database design, structures, and data mining techniques. Skilled in Kimball methodology and Data Vault modelling approaches. Strong understanding of database fundamentals and practical experience designing metadata-driven ingestion and transformation pipelines. Hands-on experience with Master Data Management (MDM), data cataloguing, and data lineage tracking. Advanced proficiency with Microsoft Azure data services, including Databricks, Azure Data More ❯

data transformations, reconciliation, and error handling. Creating and optimising data models (physical and logical) to support analytics and business intelligence. Delivering high-quality data solutions for data quality, mapping, metadata management, migration, and reporting. Supporting and maintaining existing data platforms and applications. Contributing to the development of data standards, procedures, and best practices. Participating in the evaluation and selection of 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 visualisation. More ❯

depth with strong stakeholder engagement and a passion for building future-ready data ecosystems. Key Responsibilities: Strategy & Architecture - Develop and implement enterprise-wide data governance frameworks, including ownership models, metadata strategy, and lifecycle policies Define retention, access, and usage standards across data domains Translate business needs into scalable data architecture across data lakes, warehouses, and content platforms Governance & Leadership Establish More ❯

and transformation. Advanced SQL skills and deep understanding of modern data architecture principles, especially Medallion architecture. Familiarity with orchestration tools such as Airflow. Experience implementing data quality, observability, and metadata/lineage tooling. Strong Git proficiency and experience using GitHub with CI/CD workflows. Tableau exposure Familiarity with Python for scripting and automation tasks. If you’re interested, get More ❯

Duck Creek, Insis, SICS, Insurity, or other legacy/modern platforms). Familiarity with ETL tools and processes used in data migration or data integration projects. Understanding data governance, metadata management, and data quality best practices. Experience in Agile, Scrum, or hybrid delivery methodologies. Knowledge of data warehousing, data visualization, and business intelligence tools. Bachelor's degree in Computer Science 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 ❯

drive research and discovery. Key Responsibilities Develop and maintain ETL pipelines for bioinformatics and omics datasets across cloud and on-prem environments. Standardize and harmonize diverse data sources, ensuring metadata quality and FAIR compliance. Integrate multi-modal datasets (genomic, transcriptomic, proteomic, imaging, etc.) into unified data models. Automate data validation, quality control, and lineage tracking. Support analytics, visualization, and machine 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 ❯

Agile/Scrum delivery models within enterprise-scale organisations. Excellent stakeholder management and communication skills, with the ability to translate technical concepts into business outcomes. Knowledge of data governance , metadata management , or regulatory data reporting highly desirable. Previous experience in Londn Markets, insurance , financial services , or other regulated industries is a strong advantage. More ❯

knowledge of Azure Cloud Services (DataBricks, ADF, Azure SQL, CosmosDB). Proficiency in Kimball methodology and Data Vaults. Experience with MDM, data cataloguing, and lineage management. Skilled in designing metadata-driven ingestion and transformation pipelines. Proven track record in agile delivery environments (SCRUM, PRINCE2, Lean). Excellent communication skills-able to bridge technical and non-technical audiences. Passion for emerging 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 ❯

technical leadership and guidance to cross-functional teams, ensuring best practices in data architecture, security and cloud computing Proficiency in data modelling, ETL processes, data warehousing, distributed systems and metadata systems Utilise Apache Flink and other streaming technologies to build real-time data processing systems that handle large-scale, high-throughput data Ensure all data solutions comply with industry standards 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 ❯

tools (e.g. Selenium, pytest, or custom frameworks for data testing) Knowledge of version control systems (Git, Bitbucket) and CI/CD pipelines for automated testing Understanding of data governance , metadata management , and data lineage concepts Analytical & Professional Skills High attention to detail with a structured approach to identifying data anomalies Strong analytical and problem-solving mindset for complex data ecosystems More ❯

the system that connects to customer data sources, ingests, structures, and models data, and makes it fully AI-ready. Expect technical work across: Data ingestion pipelines Data cataloging, modeling & metadata layer design High-performance data access interfaces for an AI agent Backend architecture, infra, CI/CD & multi-cloud deployments (GCP/AWS/Azure) Tech Stack Golang-first backend More ❯

working knowledge of Sage Intacct to support data discovery, reconciliation, and design activities. This role focuses on understanding existing Intacct configurations, APIs, data models, and accounting workflows — extracting critical metadata, mapping data relationships, and helping design the future accounting platform, integration and reporting architecture. Key Responsibilities Analyze and document existing Sage Intacct configurations, including chart of accounts, entities, dimensions, and More ❯

data intelligence to life. 🔍 Your Mission Architect & Implement Data Catalog Solutions — Lead the deployment and configuration of an enterprise-grade catalog (including data.world ), aligned with robust data governance frameworks. Metadata & Lineage Management — Design metadata models, lineage tracking, and glossaries; implement automated enrichment, sensitive data detection, and trust indicators. Integration & Automation — Build and maintain connectors across platforms like Snowflake, Databricks, Tableau … Power BI, and Salesforce . Implement REST APIs and event-driven automation for near real-time metadata synchronization. Data Governance Enablement — Define RBAC, data ownership models, workflows, and certification for golden datasets. Data Quality & Observability — Integrate DQ/DO tools ( Monte Carlo, Anomalo, Soda ) to visualize trust metrics and compliance dashboards. Collaboration & Adoption — Mentor teams, document best practices, and drive … adoption across engineering and business units. 🧠 What You Bring Experience in Data Architecture, Engineering, or Metadata Management. Enterprise-scale experience implementing data catalog platforms ( data.world, Collibra, Alation, Informatica EDC, etc.). Hands-on expertise with data.world at scale. Deep understanding of metadata modeling, lineage capture , and data governance frameworks (GDPR, CCPA, HIPAA). Proficiency in APIs, RESTful services, automation , and More ❯