Role Overview:
We are seeking a delivery‑focused Data Engineer to design, build, and maintain high‑quality data engineering solutions within JD Group. Reporting to a Data Engineering Area Lead, you will play a key role in developing reliable, scalable data pipelines and curated datasets that support analytics, reporting, AI, and data product use cases. This is a Maternity cover through to Apr-27.
You will work closely with other data engineers, analysts, BI developers, data scientists, and business stakeholders to ensure that data is accurate, accessible, and fit for purpose. This role is suited to an engineer who enjoys hands‑on development, takes ownership of their work, and is committed to engineering excellence and continuous improvement.
Responsibilities:
Data Engineering Delivery
* Design, build, test, and maintain data pipelines for ingestion, transformation, and curation of data from a variety of source systems
* Deliver analytics‑ready datasets and data models that are reliable, well‑structured, and easy to consume
* Work from clearly defined requirements and backlogs, contributing estimates, technical input, and delivery plans
* Take ownership of assigned data engineering tasks and deliverables, ensuring work is completed to a high standard
* Support incremental delivery and continuous improvement of data solutions
Technical Excellence & Best Practice
* Write high‑quality, maintainable, and well‑tested code using SQL, Python, and approved data engineering frameworks
* Apply data engineering standards across version control, CI/CD, testing, documentation, and observability
* Ensure pipelines are performant, scalable, and cost‑efficient within cloud environments
* Contribute to the development and reuse of common patterns, frameworks, and components
* Actively manage and reduce technical debt within owned pipelines and datasets
Data Quality, Governance & Operations
* Embed data quality checks, validation, and monitoring within pipelines
* Ensure datasets meet agreed governance, security, and access control standards
* Maintain clear documentation for pipelines, data models, and datasets
* Participate in incident investigation, root‑cause analysis, and resolution of data issues
* Support the ongoing operational health and reliability of data pipelines
Collaboration & Stakeholder Engagement
* Work closely with analysts, BI developers, and data scientists to understand data requirements and consumption needs
* Collaborate with business stakeholders to clarify requirements and validate outputs
* Communicate progress, risks, and technical considerations clearly to your Area Lead and wider team
* Contribute constructively to team ceremonies, design discussions, and code reviews
Learning & Continuous Improvement
* Continuously develop technical skills and understanding of the business domain
* Adopt and apply new tools, techniques, and patterns as agreed within the data engineering function
* Share knowledge, best practices, and learnings with the wider data engineering community
* Support and mentor junior data engineers where appropriate
Role Objectives & KPIs
* High‑quality, timely delivery of assigned data engineering work
* Reliable, well‑tested, and well‑documented data pipelines
* Improved data quality and usability across owned datasets
* Reduced incidents and faster resolution of data issues
* Positive collaboration and feedback from peers and stakeholders
* Consistent adherence to enterprise data engineering standards
Skills and Experience:
* Proven experience in a data engineering role
* Strong hands‑on experience with SQL and Python
* Experience building and maintaining data pipelines and transformations
* Understanding of data modelling for analytics and reporting use cases
* Experience working in cloud‑based data platforms, ideally GCP
* Familiarity with orchestration tools, batch processing, and structured data pipelines
* Experience with version control, CI/CD, and basic testing practices
* Ability to work independently on well‑scoped problems and deliver incrementally
* Strong attention to detail and commitment to data quality