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Senior data engineer

Bristol (City of Bristol)
Boost Talent ltd
Data engineer
Posted: 28 June
Offer description

Job Title: Senior Data Engineer

Location: UK-wide (hybrid, with occasional in-office meetings)

Salary: £75,000 (flexible depending on experience)

Hours: 35 hours per week

Employment Type: Full-time, Permanent


We’re working with a forward-thinking organisation embarking on a greenfield data transformation project. They’re looking for a Senior Data Engineer (with a strong analytics engineering background) to take ownership of a new initiative focused on building scalable, self-serve data solutions across the business.


Key Responsibilities

* Design, build, and maintain robust data transformation pipelines
* Drive a self-serve data culture through training, enablement, and best practices
* Lead stakeholder engagement to align data definitions across teams
* Own and shape the direction of the project with significant autonomy
* Coach junior team members and foster a culture of knowledge sharing
* Collaborate with architecture teams on platform design decisions
* Promote automation, efficiency, and strong data governance across the engineering function


Technical Skills & Experience

* Strong background in data transformation projects
* SQL and Python for scripting and analytics
* Airflow (or equivalent workflow orchestration tools)
* Power BI, and optionally Plotly/Dash for data visualisation
* Strong experience with AWS or equivalent
* Spark or Kafka (experience with one or the other expected)
* Proficiency in data modelling techniques such as Star or Snowflake schema
* Experience designing and deploying transformation pipelines in production
* Passion for data quality, including implementing API checks and robust validation logic
* Exposure to automation scripting in Python to streamline workflows



You’ll be someone who is passionate about data, loves solving complex problems, and enjoys mentoring others. You’ll bring a product-focused mindset and thrive in environments where you can lead initiatives and work across teams to define what "good" looks like in data engineering.

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