Junior Data Engineer in Bury

Established in 1981 with a single store in the Northwest of England, the JD Group is a leading omni‑channel retailer of Sports Fashion, Outdoors and Gyms with our colleagues working in stores across several retail fascias in many markets around the world. JD Sports Fashion Plc was listed on the London Stock Exchange in 1996 and has been a FTSE100 publicly quoted company since 2019 and continues to grow in the UK and internationally. We want to be the leading global omnichannel retailer in the sports and outdoor industry. To be a part of this successful company and help us to achieve this you will have the desire to ingrain our strategic goals of being a people‑led, innovative and customer‑focused organisation which provides operational excellence whilst identifying new areas of growth as part of our day to day objectives.

Role Overview

We are seeking a Junior Data Engineer to support the design, build, and maintenance of data engineering solutions within JD Group. Reporting to a Data Engineering Area Lead and working closely with experienced Data Engineers, you will contribute to the development of data pipelines and datasets that support analytics, reporting, AI, and data product use cases. This role is ideal for someone early in their data engineering career who is keen to develop strong technical foundations, learn modern data engineering practices, and grow their capability through hands‑on delivery, coaching, and collaboration.

Responsibilities

• Support the development and maintenance of data pipelines for ingestion, transformation, and curation of data from a range of source systems.
• Build and enhance datasets and data models under guidance, ensuring they are accurate and fit for analytics and reporting.
• Deliver well‑scoped data engineering tasks from defined backlogs, with support from more senior engineers.
• Follow agreed development practices and contribute to incremental delivery of data solutions.
• Take ownership of assigned tasks and see them through to completion.

Technical Skills & Engineering Practices

• Write clear, maintainable SQL and Python code with guidance and review from senior team members.
• Apply data engineering standards for version control, documentation, and testing.
• Learn and use approved data engineering tools, frameworks, and cloud platforms.
• Support the reuse of common patterns and components within the team.
• Actively develop understanding of pipeline performance, scalability, and cost considerations.

Data Quality, Governance & Operation

• Implement basic data quality checks and validation within pipelines.
• Ensure datasets adhere to agreed governance, security, and access control standards.
• Maintain accurate documentation for pipelines, data models, and datasets.
• Support monitoring, incident investigation, and resolution of data issues.
• Learn operational best practices to help maintain reliable data pipelines.
• Work closely with Data Engineers, analysts, BI developers, and data scientists to understand requirements.
• Participate in team ceremonies, design discussions, and code reviews.
• Communicate progress, questions, and issues clearly to your Area Lead and peers.
• Be open to feedback and actively apply it to improve quality and delivery.

Learning & Development

• Build core data engineering skills through hands‑on delivery, training, and mentoring.
• Develop understanding of the business domain and how data supports decision‑making.
• Learn modern data engineering patterns, tools, and best practices.
• Contribute to knowledge sharing within the team as experience grows.
• Work towards readiness for progression into a Data Engineer role.

Role Objectives & KPIs

• Timely and accurate delivery of assigned data engineering tasks.
• Increasing independence and quality of delivered work over time.
• Well‑documented, reliable contributions to data pipelines and datasets.
• Positive feedback from peers and managers on collaboration and learning.
• Consistent adherence to data engineering standards and best practices.
• Demonstrable progression in technical capability and confidence.

Skills and Experience

• Working knowledge of SQL and basic Python.
• Understanding of relational data concepts and data modelling fundamentals.
• Experience working with cloud data platforms or warehouses (ideally GCP).
• Familiarity with version control (e.g., Git) and basic development workflows.
• Strong desire to learn modern data engineering tools and practices.
• Ability to follow instructions, ask questions, and learn from feedback.
• Strong attention to detail and commitment to data quality.

Benefits include staff discount on JD Group and other brands within the organisation and personal development opportunities to learn and develop at work.