Senior Data Engineer

What you'll do - Data Engineering Delivery: Design, build and optimise scalable data pipelines, ETL/ELT workflows and data transformation solutions using Databricks, PySpark, SQL and dbt Cloud, delivering trusted, high-quality data products that support reporting, analytics, AI and data science. - Data Architecture & Modelling: Develop and maintain robust lakehouse architectures and reusable data models, ensuring solutions are scalable, performant, secure and aligned with enterprise data standards. - Technical Leadership & Engineering Excellence: Lead engineering initiatives, establish best practices and drive high standards across development, testing, deployment, documentation and monitoring, while championing automation and CI/CD adoption. - Code Quality & Team Mentorship: Provide technical guidance, conduct code reviews and mentor Data Engineers to strengthen engineering capability, promote knowledge sharing and foster a culture of continuous improvement. - Platform Modernisation: Play a key role in Primark’s migration from Teradata to Databricks, supporting data remodelling, pipeline migration, performance optimisation and the ongoing evolution of the enterprise data platform. - Data Product Delivery & Stakeholder Collaboration: Partner with Analytics, Data Science, Governance and business stakeholders to translate requirements into scalable technical solutions and reusable datasets that deliver measurable business value. - Continuous Improvement & Strategic Contribution: Identify opportunities to improve platform performance, scalability and cost efficiency, while contributing to engineering standards, roadmap initiatives and the long-term growth of Primark’s Data & Analytics capabilities. What you'll bring - Advanced Data Engineering Experience (5-8 Years): Strong hands-on experience designing, building and optimising enterprise-scale data pipelines and data platforms using Databricks, PySpark and advanced SQL. - Cloud Data Platforms & Architecture: Proven experience working with Azure-based data technologies and designing scalable lakehouse architectures, data models and modern cloud data solutions. - dbt & Engineering Best Practices: Experience with dbt Cloud or dbt Core, including model development, testing, documentation, lineage, deployment workflows, CI/CD and version control practices. - Data Quality, Performance & Optimisation: Strong understanding of data quality, observability, operational monitoring and performance tuning, with a focus on delivering reliable, scalable and cost-efficient data solutions. - Data Product Delivery & Commercial Value: Demonstrated success delivering data products that support analytics, reporting, AI and data science use cases, driving measurable business outcomes and value creation. - Technical Leadership & Stakeholder Engagement: Ability to provide technical leadership, translate business requirements into scalable solutions and collaborate effectively across Engineering, Analytics, Governance, Architecture and business teams. - Mentoring & Continuous Improvement: Experience coaching engineers, sharing best practices and driving continuous improvement across data engineering teams.