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Data Analytics Engineer, UK

Maximus
Remote, GBR
Remote
Discovered 1 weeks ago
Analytics engineeringSQLData modellingFact and dimension modellingSemantic modellingData quality testing

Full Job Posting

Role overview

Create trusted, consistent, well-modelled, documented, and reusable data products for BI, analytics, operational reporting, and future AI-enabled use cases.

Bridge data engineering, data architecture, and BI by translating business rules, metrics, and analytical requirements into scalable models, semantic layers, and transformation logic.

Improve decision-making through version control, testing, documentation, data quality checks, and clear ownership of shared business definitions.

Key responsibilities

  • Translate reporting and decision needs into reusable analytics requirements and data product specifications with business and technical stakeholders.
  • Design, build, and maintain curated datasets, fact/dimension models, reusable metric logic, and semantic structures for self-service analytics.
  • Develop modular SQL transformations with transparent, version-controlled, reusable, maintainable business rules.
  • Partner with data engineers on upstream pipelines, source-to-target requirements, data-quality issues, and downstream data suitability.
  • Implement data tests, reconciliation checks, metric validation, documentation reviews, and controlled releases.
  • Document model definitions, lineage, business rules, ownership, limitations, and usage guidance.
  • 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 skills and experience building reusable, tested, documented transformation logic.
  • Experience designing analytics-ready models, curated datasets, fact/dimension models, or semantic datasets.
  • Experience with modern cloud data platforms such as Microsoft Fabric, Synapse, Databricks, Snowflake, or equivalent.
  • Understanding of data quality, lineage, governance, privacy, and access control in analytical environments.
  • Experience with Power BI or similar BI tools and model design for reporting performance and usability.
  • Experience with dbt, SQLMesh, Fabric semantic models, or equivalent analytics engineering tools; ability to collaborate across technical and business teams.

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.

Stakeholders and travel

Works with BI and Analytics, Data Engineering, the Data Architect, Data Governance and Quality, business product owners, operational subject-matter experts, and technology teams.

External stakeholders may include data suppliers and partner organisations.

The source lists business travel of 10,000+ miles per year as a yes/no item.

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