> Markdown version of [/jobs/ext/366609-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/366609-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). --- # Analytics Engineer - **Company:** i6 Group - **Location:** Manchester, UK (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Query Performance, Airflow, Data Analysis, BigQuery, Cloud Database, Computer Programming, Continuous Integration, Data as a Services, Information Engineering, Software Debugging, Document-Oriented Databases, Github, Jinja (Template Engine), Python (Programming Language), NoSQL, Performance Tuning, DataOps, Software Engineering, SQL Databases, Data Processing, Google Cloud, Macros, System Availability, Snowflake, Git, Containerization, Git Flow, Low Latency, Terraform, Software Version Control, Data Pipelines, Docker - **Published:** June 24, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=ff5fee7db3441bc7 ## About the Role Do you have experience in Terraform?, * 3-4+ years of experience in Data or Analytics Engineering. * Expert SQL: Ability to write complex window functions, optimise joins, and debug query plans. * dbt Expertise: Deep hands-on experience in production (snapshots, incremental models, custom generic tests, Jinja/Macros). * Cloud Data Warehousing: Deep understanding of BigQuery or Snowflake, specifically clustering and partitioning strategies. * Version Control: Expert-level comfort with Git, branching strategies, and Pull Request workflows. * Orchestration: Experience with Airflow, Dagster, or similar tools. * NoSQL Knowledge: Understanding of NoSQL structures and how to transform them into relational models. * Data Contracts: Understanding of data contracts and their role in pipeline stability. You will be a great fit for this role if in addition to the above you have the following: * Experience with Google Cloud Platform (GCP) and its data services (BigQuery). * Familiarity with Infrastructure-as-Code (Terraform). * Experience with Containerization (Docker). * Programming proficiency in Python for automation and data manipulation. ## Description In your new role as an Analytics Engineer at i6 you will be responsible for designing, building, and maintaining complex data models using dbt (Jinja, Macros, Incremental strategies) and managing high-availability ingestion pipelines. You will focus on the "build" phase of the data lifecycle-implementing DataOps best practices, including CI/CD via GitHub Actions and automated testing frameworks. You will optimise warehouse performance (BigQuery/Snowflake) to support millions of rows of data and collaborate across teams to ensure data contracts are met and data quality is guaranteed before it reaches the end-user. What you will do * Own the Transformation Layer: Design, build, and maintain complex dbt models to power internal BI and external customer analytics. * Pipeline Management: Manage and monitor data ingestion pipelines to ensure high availability and low latency. * Performance Tuning: Optimise cloud data warehouse costs and query performance (clustering, partitioning) for sub-second response times. * Data Quality & Testing: Build and maintain automated testing frameworks (dbt test, Great Expectations) to proactively catch data issues. * DataOps: Maintain CI/CD pipelines (GitHub Actions) for data deployment, applying software engineering principles to data workflows. * Collaboration: Partner with Data Analysts to provide clean models and work with the Data Engineering Lead on architectural infrastructure decisions. * Technical Documentation: Document data models, macros, and transformation logic clearly to ensure team scalability. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Software Engineer Salary London](https://www.wearedevelopers.com/magazine/252-software-engineer-salary-london) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)