> Markdown version of [/jobs/ext/4596139-data-and-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/4596139-data-and-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data and Analytics Engineer

- **Company:** Hireful
- **Location:** London, UK
- **Salary:** £83,492.0
- **Contract:** Permanent contract
- **Skills:** Third Normal Form, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Business Intelligence Development, BigQuery, Code Review, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Visualization, Database Queries, DevOps, Dimensional Modeling, Middleware, Github, Power BI, Cloud Services, SQL Server Analysis Services, Parquet, Google Cloud, Warehouse Management Systems, Test-Driven Development (TDD), Snowflake, Indexer, Git, Deployment Automation, Data Analytics, Google Bigquery, Restful APIs, Terraform, Looker Analytics, Software Version Control, Data Pipelines, Databricks
- **Published:** November 6, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5954699932

## About the Role

* Solid foundations in Data Engineering, BI Development or Analytics Engineering, with a strong desire to develop and grow alongside the platform
 * Strong SQL skills and a solid understanding of data-modelling principles (e.g. dimensional modelling, 3NF)

 * Working knowledge of SQL Server or a comparable relational engine beyond writing queries: execution plans, indexing and locking
 * Experience in ELT/ELT and extracting from a live transactional system without disrupting it, and working to production change control

 * Experience designing and building modern data pipelines and Lakehouse architectures
 * Hands-on experience with at least one enterprise-grade data platform (e.g., Google BigQuery, Snowflake, Databricks, or equivalent)
 * Familiarity with cloud services and deployment automation (e.g., GCP, AWS, Terraform, CI/CD, Git)
 * Ability to deliver clear, insightful, and performant data visualisations and dashboards using tools such as Looker, Omni, Power BI, or other modern BI platforms
 * Agile mindset: embraces iterative delivery, peer code reviews, and test-driven development
 * Excellent stakeholder engagement and communication skills

Desirable:

 * Experience with Lakehouse patterns; setting standards, quality checks etc.
 * Parquet partitioning and clustering to optimise for query speed and cost
 * GitHub Actions
 * REST APIs
 * ML Ops tooling

## Description

+ Design relational & lakehouse schemas in a modern cloud warehouse (e.g., BigQuery, Snowflake) using dbt.
 + Work closely with our consultants on the re-architecture of SSAS cubes to modern Lakehouse models, a great hands-on learning opportunity.
 + Set up layered, tested dbt models (staging through to governed marts) and ensure data pipelines are optimal and maintained.
 + Contribute to embedding the new data platform and architecture in the business and working to ensure our investment is maximised.
 * Data Engineering
 + Enhance, develop, and maintain ETL / ELT, middleware, and integrations.
 + Ingest data from WMS, ERP, ecommerce, IoT and ad-hoc sources (CSV, APIs, unstructured).
 * Analytics & Reporting
 + Model data (dbt and a governed semantic layer) and build stunning dashboards used by Sales, Buying, Ops & Finance
 + Optimise datasets for performance, cost, and freshness (partitioning, clustering, and incremental models).
 * AI / ML Enablement
 + Work with Liberty's AI & Automation Dev Team to prepare and expose curated data to our AI/ML toolsets and workflows.
 * Stakeholder Enablement & Documentation
 + Maintain clear documentation for BI projects, processes, and solutions.
 + Enforce data governance practices to ensure compliance with organizational standards.
 + Upskill analysts through show-and-tell sessions and guard-rail documentation.
 * Governance & DevOps
 + Monitor and improve CI/CD processes, source control (Git), and automate dbt and BI platform deployments.
 + Adhere to and maintain data-quality rules, lineage, and catalogue standards.