Data Analytics Engineer, UK

Role overview

Responsibilities

• Translate reporting and decision-making needs into reusable analytics requirements and data product specifications with business and technical teams.
• Design and maintain analytics-ready models, curated datasets, fact and dimension models, reusable metrics, and semantic structures.
• Develop modular, version-controlled SQL transformation logic that is reusable and maintainable.
• Partner with data engineers on upstream pipelines, source-to-target requirements, data quality issues, and downstream data fitness.
• Implement data tests, reconciliation checks, metric validation, documentation reviews, and controlled releases.
• Document model definitions, lineage, business rules, ownership, limitations, and appropriate usage.
• Champion reusable data products, semantic modelling, automation, AI-ready foundations, and reduced duplicated logic.

Essential requirements

• Degree or equivalent experience in data, analytics, computer science, or a related field.
• 5+ years in data, including 2+ years in analytics engineering, BI engineering, or data modelling.
• Advanced SQL and experience building reusable, tested, documented transformation logic.
• Experience designing analytics-ready models, including curated datasets, fact/dimension models, or semantic datasets.
• Experience with a modern cloud data platform such as Microsoft Fabric, Synapse, Databricks, Snowflake, or equivalent.
• Understanding of data quality, lineage, governance, privacy, and access control.
• Experience with Power BI or similar BI tools and awareness of model effects on reporting performance and usability.
• Experience with dbt, SQLMesh, Fabric semantic models, or equivalent analytics engineering tools.

Desirable experience

• Experience building governed data products in regulated or complex environments.
• Experience with data catalogues, lineage, metadata, or business glossary tools.
• Exposure to AI-ready datasets, advanced analytics, or machine learning use cases.

Key competencies

• Work confidently with data engineers, data architects, BI teams, and business stakeholders to deliver trusted data products.
• The description also lists respect, effective partnership, innovative solutions, customer focus, compassion, responsibility, integrity, communication, data-driven decision-making, and adaptability.