Senior Data Engineer

Description

The company is a data-driven organisation, and this senior role is about designing, building and evolving the core pipelines and platforms behind its analytics, reporting and data science work.

Your work will make the wider data estate more reliable and quicker to deliver insight from, cut down manual effort, and set the standard for how data engineering is done across the company.

Responsibilities

• Lead the building and tuning of ingestion and transformation pipelines, using tools such as Spark, Scala, Python, AWS, DBT and Snowflake.
• Design and evolve the Data Lake so it stays robust, scalable, secure and ready for what comes next.
• Own the ETL processing, table refreshes, defect fixes and other day-to-day operational work, and automate it to reduce manual effort.
• Improve performance through concurrency, multi-threading, automation and proven engineering patterns.
• Guide Agile delivery, mentor engineers, and get high-quality cloud-native solutions shipped through CI/CD.
• Bring together data from several sources and solve complex problems with technical depth and a strategic view.
• Shape data engineering standards, patterns and governance across the wider organisation.

Requirements

• Hands-on experience using DBT to design, build and improve transformations in a complex data environment.
• Engineering experience with Python and SQL, and the ability to solve complex data problems at scale.
• The ability to design and guide modern data engineering solutions, including data modelling, ETL/ELT, data warehousing and scalable pipelines.
• The ability to lead and develop data engineers, pairing hands-on work with coaching, technical direction and ownership of delivery.
• Curiosity about AI and automation, and an interest in how new technologies could improve engineering quality, productivity and ways of working.
• A plus: experience with AWS or another major cloud platform. The team uses EMR, Athena, Lambda, S3, Glue and Step Functions, but transferable experience on similar problems is welcome.
• A plus: experience with Snowflake, Spark, Scala or Airflow, or comparable technologies and similar data engineering challenges.
• A plus: experience with Git, GitHub and CI/CD practices, including tools such as GitHub Actions.
• A plus: knowledge of real-time data processing, data governance, data quality or observability.
• A plus: experience using AI-enabled engineering tools or automation to improve data engineering productivity, quality or reliability.
• You are encouraged to apply even if you do not meet every requirement, as long as you have strong data engineering foundations, hands-on DBT experience and experience solving complex problems at scale.

Conditions

Hybrid working.

Permanent contract.

The company is an equal opportunity employer and does not discriminate on the basis of protected characteristics.

Reasonable adjustments are available during the application and interview process on request.