> Markdown version of [/jobs/ext/4612338-principal-data-engineer](https://www.wearedevelopers.com/jobs/ext/4612338-principal-data-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). --- # Principal Data Engineer - **Company:** Via - **Location:** London, UK - **Experience:** Expert - **Salary:** £90,000 - £125,000 - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Amazon S3, Automation of Tests, BigQuery, Cloud Computing, Continuous Integration, Data Architecture, Information Engineering, Github, Python (Programming Language), Node.Js, Power BI, Data Processing, Sql Optimization, Snowflake, Gitlab, Data Management, Functional Programming, Amazon Simple Queue Service (SQS), Terraform - **Published:** November 7, 2026 - **Apply:** https://www.reed.co.uk/jobs/principal-data-engineer/57540170 ## About the Role * 6-8+ years' experience in data engineering, including technical leadership * Strong experience with Snowflake, Redshift or BigQuery (Snowflake preferred) * Hands-on AWS experience, including S3, Lambda, Kinesis, SQS or Firehose * Experience with dbt, Python or Node.js * Advanced SQL and data modelling skills * Experience with CI/CD, GitHub/GitLab and Terraform * Proven ability to mentor and manage technical teams Experience with Power BI, ThoughtSpot, automated testing, cloud cost optimisation or vendor management would be advantageous. ## Description You'll shape technical strategy, mentor senior engineers and build scalable data solutions supporting commercial, customer and operational decisions. What you'll be doing * Lead the design and development of modern cloud data platforms and pipelines * Provide technical leadership, line management and mentoring to data engineers * Drive data architecture, engineering standards and best practices * Manage technical risks, delivery quality and platform performance * Optimise cloud infrastructure costs and data processing efficiency * Collaborate with product, engineering and data quality teams