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# Data Management Analyst

- **Company:** Core Consultants
- **Location:** London, UK
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Profiling, Information Engineering, Data Governance, Digital Assets, Monitoring of Systems, Information Lifecycle Management, Python (Programming Language), Machine Learning, Metadata, Meta-Data Management, Metadata Standards, Cloud Services, Standard Sql, Data Streaming, Unstructured Data, Data Processing, Delivery Pipeline, Model Validation, Collibra, Data Management, Drift Detection, Data Pipelines
- **Published:** November 3, 2026
- **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-453C14AB0AD9055684/

## About the Role

* Proven experience in data quality, metadata management, or data governance roles.
 * Hands-on experience with data quality and data cataloguing tooling, including rule engines and profiling tools, metadata/catalogue platforms, lineage capture, glossary management, and metadata enrichment workflows
 * Strong understanding of metadata concepts: classification, lineage, taxonomies, ontology modelling, glossary management.
 * Strong SQL and Python skills for analysis, profiling, reconciliation, and control development.
 * Understanding of how data flows through a modern cloud data platform (Azure), including lakehouse layers, ingestion patterns, and modelling approaches.
 * Experience working with structured and unstructured datasets.
 * Understanding of data lifecycle management, authoritative sources, and data control frameworks.
 * Ability to interpret complex data issues and translate findings into actionable improvement plans.
 * Excellent communication and stakeholder management skills.
 * Strong attention to detail and ability to manage multiple priorities.
 * Comfortable operating in a maturing data environment and shaping new processes and standards.

Desired Skills

 * Knowledge of DAMA-DMBOK or other recognised data management frameworks.
 * Familiarity with GDPR, data protection obligations, and responsible AI principles.
 * Experience in regulated industries (insurance, financial services, healthcare).
 * Understanding of data risk management and control assurance.
 * Experience with metadata/cataloguing tools (Purview, Collibra, Informatica).
 * Working knowledge of cloud data platforms (eg, Azure) and modern data engineering concepts.
 * Experience contributing to data governance forums or data councils.
 * Exposure to AI/ML concepts such as model monitoring, fairness/bias techniques, drift detection, hold-out testing, cross-validation, and performance measurement.

## Description

We're looking for a Data Management Analyst to help build the data foundations and ensure our clients data is accurate, consistent, well-catalogued, and trusted. You'll play a key role in improving data quality, strengthening metadata and lineage management, and supporting responsible data use across the organisation. Working closely with Data Owners, Stewards, and technical teams, you'll help make the clients data transparent, compliant, and ready for analytics, automation, and future AI capabilities.

What You Will Do

 * Deliver the Data Quality and Data Under Governance programme, including profiling, validation, reconciliation, and root-cause analysis across structured and unstructured datasets.
 * Identify and document Critical Data Elements (CDEs), including definitions, ownership, business rules, and authoritative sources.
 * Design and maintain data quality rules, thresholds, and controls using enterprise DQ tooling.
 * Define data quality metrics, support remediation workflows, and drive improvement plans with Data Stewards and business teams.
 * Maintain and enhance the enterprise data catalogue, ensuring data assets are registered, discoverable, and well-documented.
 * Implement and maintain metadata standards (classification, lineage, taxonomies, ontology structures, ownership, usage guidance).
 * Build and maintain business glossaries and ensure metadata accuracy and regulatory alignment.
 * Promote catalogue adoption and metadata literacy across business domains.
 * Partner with Data Engineering to embed DQ rules and metadata capture into automated pipelines and platform-native tooling.
 * Collaborate with technology teams to embed lineage capture into data pipelines and platforms.
 * Support operational remediation activities and contribute to governance forums, working groups, and Data Councils.
 * Provide recommendations to improve data handling, control design, and process efficiency.
 * Support responsible AI readiness by ensuring high-quality, well-documented data foundations.