Data Engineer

The Opportunity

STARK UK is transforming its data platform and engineering capability to support the next generation of analytics, self-service reporting, AI, automation and data-driven decision making.

We are seeking a hands-on Data Engineer to join our Data Engineering team and help build, improve and support the trusted data products that underpin decision making across STARK UK.

Reporting to the Lead Data Engineer, you will work within a team moving from Informatica and Azure Synapse-based solutions towards a modern Databricks-centric platform. You will design and build scalable data pipelines, transformations and business-ready datasets, while helping migrate existing solutions and improve engineering standards.

The role covers the full data product lifecycle, from source ingestion through Bronze, Silver, Gold and Platinum data assets to curated datasets that support reporting, self-service analytics and future AI initiatives. You will work closely with other Data Engineers, Data Analysts, business stakeholders and delivery partners to turn business requirements into reliable, reusable data products.

This is an opportunity to develop deep Databricks capability while contributing practical experience of Azure Synapse and Python during a significant period of platform modernisation.

Key Responsibilities

Data Pipeline and Data Product Engineering

Design, build, test and maintain scalable data pipelines and data products using Databricks, Python, SQL and Azure data technologies.

Ingest, transform and curate data from operational systems, files, APIs and other internal or external sources.

Build and maintain data assets across Bronze, Silver, Gold and Platinum layers, following agreed architecture and engineering standards.

Develop reusable, business-ready datasets that support analytics, reporting, semantic models and self-service use cases.

Apply appropriate data modelling and transformation techniques to produce clear, performant and maintainable data structures.

Ensure solutions are documented, version-controlled, testable and supportable.

Platform Modernisation

Contribute to the transition from Informatica and Azure Synapse-based solutions to Databricks, including analysis, migration, validation and decommissioning activity.

Use existing Synapse knowledge to understand current pipelines and architecture, identify dependencies and support safe migration to the target platform.

Refactor or rebuild legacy data processes where appropriate, improving scalability, maintainability and operational resilience.

Work with the Lead Data Engineer, Architecture and delivery partners to adopt agreed Databricks patterns and standards.

Support effective knowledge transfer from System Integrators and other third parties, building internal team capability and reducing long-term dependency.

Engineering Quality and Operational Excellence

Build data quality controls, automated tests, monitoring and alerting into engineering solutions.

Follow DataOps, CI/CD, source-control and deployment practices, contributing to their continued improvement.

Investigate and resolve data pipeline failures, quality issues and performance problems, including appropriate operational support for production data services.

Identify technical debt and manual processes and contribute practical improvements that reduce risk and support effort.

Maintain clear technical documentation, lineage and operational knowledge so that solutions can be understood and supported across the team.

Ensure engineering work complies with security, governance and data management standards.

Collaboration and Business Delivery

Work with Data Analysts and business stakeholders to understand data requirements and translate them into well-designed engineering solutions.

Partner with other Data Engineers through code reviews, problem solving and knowledge sharing.

Collaborate with Architecture, Insight & Analytics, Testing and Technology teams to deliver joined-up solutions.

Communicate delivery progress, risks, dependencies and technical issues clearly to the Lead Data Engineer and relevant stakeholders.

Develop an understanding of STARK UK business processes and data domains so that engineering decisions reflect business context as well as technical requirements.

What Success Looks Like

Reliable, scalable and well-documented data pipelines and data products are delivered to agreed standards.

Databricks is used effectively as the strategic engineering platform, with growing depth of capability and consistent adoption of team standards.

Legacy Informatica and Synapse solutions are understood and migrated safely, with dependencies and operational risks actively managed.

Data quality, testing, monitoring and documentation improve across the products you own.

Business users and analysts have access to trusted, reusable datasets that meet reporting and analytics needs.

Production issues are resolved effectively and recurring problems are addressed through sustainable engineering improvements.

Knowledge is shared across the team, reducing key-person dependency and strengthening internal capability.

You are recognised as a dependable engineering partner who takes ownership of delivery and the ongoing health of your data products.

Skills & Experience Required

Hands-on experience in a Data Engineering, Data Platform or Data Warehouse role.

Strong hands-on Databricks experience, including building data pipelines and transformations in a Lakehouse environment.

Strong Python skills for data engineering and transformation workloads.

Hands-on Azure Synapse experience and the ability to understand, maintain and migrate existing Synapse-based data solutions.

Strong SQL skills and practical experience of data modelling and transformation.

Experience designing, building and supporting scalable batch data pipelines and data products.

Understanding of data quality, testing, monitoring, version control and deployment practices.

Experience working with cloud data storage and modern data platform concepts.

Ability to diagnose technical and data issues and take ownership through to resolution.

Clear communication skills and the ability to work effectively with technical colleagues, analysts and business stakeholders.

Desirable

Experience migrating data pipelines or workloads from legacy ETL platforms to Databricks.

Experience with Informatica or similar enterprise integration tooling.

Experience with Azure Data Lake and related Azure data services.

Practical experience of Delta Lake, medallion or Lakehouse architecture patterns.

Experience with CI/CD, automated testing and DataOps practices.

Knowledge of Power BI semantic models, business data marts or analytics-ready datasets.

Experience working with ERP data and commercial domains such as sales, pricing, margin, inventory, customer, product or supplier data.

Experience within retail, distribution, manufacturing or supply chain environments.

Why Join?

Join STARK UK during a significant modernisation of its Data & Analytics capability.

Work hands-on with Databricks while helping move the organisation away from legacy data platforms.

Build trusted data products that support analytics, automation, self-service reporting and future AI initiatives.

Develop your engineering capability within an established team, with support from the Lead Data Engineer and Head of Data Engineering.

Contribute to modern engineering standards and ways of working that will shape how data is delivered across STARK UK.

Work on data used across multiple brands and business areas, with clear links between engineering delivery and business outcomes.

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