101 to 125 of 393 Metadata Jobs in the North of England

of data models , lineage , and governance frameworks that ensure data trust, accuracy, and traceability. Define architecture standards for data integration , master data management , and metadata strategy . Support key programmes like ITSM (ServiceNow) and back-office platform migrations , ensuring the right data gets to the right place at the right More ❯

of data models , lineage , and governance frameworks that ensure data trust, accuracy, and traceability. Define architecture standards for data integration , master data management , and metadata strategy . Support key programmes like ITSM (ServiceNow) and back-office platform migrations , ensuring the right data gets to the right place at the right More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

and geospatial data. • Collaborate with ML and research teams to ensure data pipelines align with model training requirements and schedules. • Develop and maintain robust metadata management and data versioning strategies. • Optimise data workflows for performance, reproducibility, and cost efficiency. • Implement automated processes for data quality checks, validation, and governance. • Champion More ❯

quality across the data ecosystem. These sources include Adalyser, Meta Ads, Google Ads, Hubspot and Aircall along with performance tracking data, product imagery and metadata from bespoke platforms. Data Quality & Validation: Implement processes to monitor and ensure the accuracy, completeness, and consistency of data. Conduct regular data audits and resolve More ❯

quality across the data ecosystem. These sources include Adalyser, Meta Ads, Google Ads, Hubspot and Aircall along with performance tracking data, product imagery and metadata from bespoke platforms. Data Quality & Validation: Implement processes to monitor and ensure the accuracy, completeness, and consistency of data. Conduct regular data audits and resolve More ❯

quality across the data ecosystem. These sources include Adalyser, Meta Ads, Google Ads, Hubspot and Aircall along with performance tracking data, product imagery and metadata from bespoke platforms. Data Quality & Validation: Implement processes to monitor and ensure the accuracy, completeness, and consistency of data. Conduct regular data audits and resolve More ❯

quality across the data ecosystem. These sources include Adalyser, Meta Ads, Google Ads, Hubspot and Aircall along with performance tracking data, product imagery and metadata from bespoke platforms. Data Quality & Validation: Implement processes to monitor and ensure the accuracy, completeness, and consistency of data. Conduct regular data audits and resolve More ❯

quality across the data ecosystem. These sources include Adalyser, Meta Ads, Google Ads, Hubspot and Aircall along with performance tracking data, product imagery and metadata from bespoke platforms. Data Quality & Validation: Implement processes to monitor and ensure the accuracy, completeness, and consistency of data. Conduct regular data audits and resolve More ❯

quality monitoring metrics and data quality rule creation. Operation and incremental build-out of the data platform using in-house and vendor data tools. Metadata analysis and data catalogue curation to support data discoverability. Day-to-day data subject matter expert support for the front office across various data sets. More ❯

quality monitoring metrics and data quality rule creation. Operation and incremental build-out of the data platform using in-house and vendor data tools. Metadata analysis and data catalogue curation to support data discoverability. Day-to-day data subject matter expert support for the front office across various data sets. More ❯