> Markdown version of [/jobs/ext/3594282-data-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3594282-data-ai-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 & AI Engineer - **Company:** Tenth Revolution Group - **Location:** Manchester, UK - **Salary:** £40,000.0 - £50,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Data Governance, Extract Transform Load (ETL), Data Systems, Data Warehousing, Python (Programming Language), Standard Sql, Data Streaming, Apache Spark, Data Lakes, Pyspark, Data Pipelines, Databricks - **Published:** October 6, 2026 - **Apply:** https://www.reed.co.uk/jobs/data-ai-engineer/57427198 ## About the Role * Experience developing data engineering solutions on cloud platforms * Strong SQL and Python skills. * Experience with Databricks, Delta Lake and Spark (PySpark or Scala). * Knowledge of modern data warehousing or lakehouse architectures. * Hands-on experience developing production-grade data pipelines. * Understanding of data modelling and ETL/ELT best practices. ## Description Are you passionate about building modern data platforms and working with the latest Data & AI technologies? Do you want to play a key role in delivering large-scale data transformation projects for some of the UK's most recognised organisations?, As a Databricks Data & AI Engineer, you will be responsible for designing, developing and optimising modern data solutions within a cloud-first environment., * Designing and building scalable batch and streaming data pipelines using Databricks and Spark. * Developing robust ETL/ELT solutions and data products to support analytics, reporting and AI initiatives. * Working closely with stakeholders to understand business requirements and translate them into technical solutions. * Building and maintaining data models within modern lakehouse architectures. * Optimising platform performance, scalability, reliability and cost efficiency. * Implementing data governance, security and data quality standards. * Supporting AI and machine learning initiatives by creating trusted, reusable data foundations.