1 to 25 of 67 Permanent Metadata Jobs in the East of England

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

and implementing scalable data pipelines and integration patterns across structured and unstructured data sources (e.g., Azure SQL, MySQL, MongoDB). Familiarity with data governance, metadata management, and data quality frameworks. Practical experience applying DevOps principles to data engineering, including CI/CD pipelines, infrastructure as code, and monitoring. Solid understanding More ❯

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 More ❯

and automate reporting processes. Design frameworks for data validation, reconciliation, and auditing to ensure high trust in people metrics. Contribute to the development of metadata, documentation, and master data definitions for people data domains (e.g., job architecture, organisation structure). Support HR and business analysts in accessing, joining, and transforming More ❯

preferred. Strong analytic skills related to working with structured, semi-structured, and unstructured datasets and blob storage. Build processes supporting data transformation, data structures, metadata, dependency and workload management. A successful history of manipulating, processing and extracting value from large disconnected datasets. Strong project management and organizational skills. Experience supporting More ❯

prediction to find new medicines. We are a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data/metadata/knowledge platforms, and AI/ML and analysis platforms, all geared toward: Building a next-generation data experience for GSK's scientists, engineers, and More ❯

embedded AI and machine learning. Deliver data solutions focused on business fulfilment, operational efficiency, and measurable impact. Champion best practices in data quality, governance, metadata, and regulatory compliance. Partner across functions and lead a high-performing data engineering team, fostering innovation and agility. Skills and experience we're looking for More ❯

to prepare, annotate, store and navigate their datasets, including data application design and improvement. Define and document best practices for capturing and entering experimental metadata, and educate scientists and collaborators about these standards. Build pipelines for quality control, processing and analysis of raw targeted and un-targeted datasets. Develops and More ❯

the following libraries: Numpy, Pandas, PySpark and Spark SQL - Expert knowledge of ML Ops frameworks in the following categories: a) experiment tracking and model metadata management (e.g. MLflow) b) orchestration of ML workflows (e.g. Metaflow) c) data and pipeline versioning (e.g. Data Version Control) d) model deployment, serving and monitoring More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯

Policies. To be successful you will need: • Proven experience in a similar senior technical role • To understand the importance of digital asset management and metadata, i.e. you ‘get the point’ of LibraryLink, preferably derived from direct experience of managing significant digital collections • A thorough understanding of digital asset formats and More ❯