Data Engineer

Leap29; England; On-site; GBP 60,000 - 90,000; Full time

Job summary

Leap29 is seeking a Data Engineer in the United Kingdom to design and implement scalable data pipelines and standardise data across models. You will apply Python development, best practices, and collaborate with cross-functional teams in an Agile environment.

The role emphasizes working with cloud platforms, big data technologies such as Spark/Databricks, and modern data architectures to deliver robust data solutions.

Qualifications

• Experience in complex, large-scale data environments.
• Strong Python skills and experience building data pipelines.
• Knowledge of data modelling and transforming data between formats.
• Familiarity with cloud platforms and modern data architectures including Spark and Databricks.

Responsibilities

• Design, build and maintain scalable data pipelines.
• Transform and standardise data across different data models.
• Develop reusable, maintainable solutions using Python and software engineering best practices.
• Work with cloud platforms, distributed processing technologies and modern data architectures.
• Collaborate with engineers and stakeholders in an Agile environment.

Skills

Data Engineering

Python

Cloud platforms

Data modelling

Tools

Docker

Kubernetes

Git/GitHub

Linux/Unix

Spark

Databricks

Parquet

Delta Lake

Job description

Data Engineer

Key Responsibilities

• Design, build and maintain scalable data pipelines.
• Transform and standardise data across different data models.
• Develop reusable, maintainable solutions using Python and software engineering best practices.
• Work with cloud platforms, distributed processing technologies and modern data architectures.
• Collaborate with engineers and stakeholders in an Agile environment.

Key Skills & Experience

• Proven Data Engineering experience in complex, large-scale environments.
• Strong Python skills and experience building data pipelines.
• Experience with data modelling and transforming data between different formats.
• Knowledge of cloud platforms, ideally Azure, alongside Spark and Databricks.
• Familiarity with data storage technologies such as Parquet and Delta Lake.
• Experience with Docker, Kubernetes, Git/GitHub and Linux/Unix environments.
• Ability to contribute to shared codebases and collaborative development teams.