Data Engineer

🔒 Confidential Employer
Posted 21 April 2026
LOCATION
London
TYPE
Full-time
LEVEL
Mid-Senior level
SALARY
£70,000 / year
CATEGORY
Technology
This employer holds a UK Home Office sponsor license — sponsorship for this specific role is at the employer’s discretion

SKILLS

SQL Python Dagster dbt Data Pipelines Git Data Modeling

FULL DESCRIPTION

# Data Engineer

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We’re looking for a Mid-Level Data Engineer to join our team to design, build, and maintain the data pipelines, models, and platforms that power insights and decision-making across [Employer hidden — view at passion-project.co.uk].

What you’ll be doing:

  • Building robust and scalable data extraction pipelines using dlthub, getting data from our diverse sources (e.g. internal platform, third- and first-party APIs, etc.) into our existing pipeline infrastructure
  • Creating, maintaining and optimising data models using dbt, turning data into usable, enriched datasets for internal self-service reporting and analytics use
  • Working with Dagster as our orchestration platform to manage pipeline dependencies, scheduling, and monitoring
  • Partnering with data scientists and analysts to make sure that datasets are meaningful, intuitive and relevant for their use cases, particularly for consumption via Lightdash (our BI tool)
  • Creating clear data governance principles and practices, being an advocate for high quality data creation and usage across the business
  • Contributing to data platform infrastructure management and optimisation

What you’ll bring:

  • Proven experience in a data engineering and/or analytics engineering role, with a track record of having sustained impact
  • Experience with modern orchestration tools (Dagster preferred, Airflow or similar also welcome) for managing complex data workflows
  • Excellent SQL and strong Python expertise - you’ll be writing custom data assets in Dagster and contributing to dbt pipelines
  • Experience with core git workflows and version control best practices
  • Experience working cross-functionally and building strong relationships with data users of varying degrees of data literacy
  • Comfortable with ambiguity and excited by the opportunity to shape how data drives decision-making at [Employer hidden]
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