Commercial Senior Data Engineer

- Strong understanding of software engineering principles, cloud architecture, information risk, data security, and governance, with the ability to apply these principles pragmatically in complex, enterprise‑scale environments. - Extensive hands‑on experience in modern programming and scripting languages commonly used in data engineering (e.g., Python, SQL, PySpark; additional languages such as Java or .NET beneficial), with a strong focus on readable, maintainable, and performant code. - Recognised senior‑level engineering expertise in data engineering and cloud platforms, demonstrated through successful ownership of production systems, architectural decision‑making, mentoring of other engineers, and, where applicable, relevant professional certifications (e.g., Azure Data Engineer, Cloud Solution Architect).

Your role The Engineer develops technology capabilities and services that Product teams use to build customer-facing solutions, expediting the delivery of technology-enabled value to the Business. They develop and/or configure technologies to meet technical requirements and architecture, whilst ensuring the services they build are robust, maintainable, reusable, and highly scalable. Engineer has ownership of end-to-end data pipelines (semantic layer -> bespoke layer), do architectural decision within Azure Data Platform, owns non-functional requirements such as performance improvement, cost reduction and security standards, providing long-term technical direction for data engineering within Product Team. Main Responsibilities:

Main Responsibilities: - Designs, configures, develops, builds, tests, and releases end‑to‑end Azure‑based data solutions and services that meet functional and non‑functional requirements, contributing to the delivery of high‑value, scalable Product increments. - Leads the design and implementation of data engineering solutions and architectural components across the Azure Data Platform (e.g., Azure Data Factory, Databricks, Snowflake, SAP BW, MuleSoft, Apigee), ensuring alignment with enterprise architecture standards and best practices.

- Designs, configures, develops, builds, tests, and releases end‑to‑end Azure‑based data solutions and services that meet functional and non‑functional requirements, contributing to the delivery of high‑value, scalable Product increments. - Leads the design and implementation of data engineering solutions and architectural components across the Azure Data Platform (e.g., Azure Data Factory, Databricks, Snowflake, SAP BW, MuleSoft, Apigee), ensuring alignment with enterprise architecture standards and best practices. - Acts as a technical authority for data engineering, collaborating closely with Product Owners, Platform Owners, Platform Architects, and stakeholders to translate business requirements into robust, maintainable, and cost‑efficient data solutions.

To succeed, you will need: - Proven track record of independently delivering and owning high‑quality, secure, and scalable technology solutions that meet complex functional and non‑functional requirements within modern Azure‑based data architectures. - Advanced experience with end‑to‑end solution quality assurance, including data validation, automated testing strategies, performance testing, and production readiness to ensure long‑term scalability, reliability, and maintainability.

- Advanced experience with end‑to‑end solution quality assurance, including data validation, automated testing strategies, performance testing, and production readiness to ensure long‑term scalability, reliability, and maintainability. - Deep, hands‑on expertise in the Azure Data Platform, including design, development, and operation of data pipelines and platforms using technologies such as Azure Data Factory, Databricks, Delta Lake, Snowflake, and related Azure services. - Strong experience designing and applying DevOps and DataOps practices for data platforms, including CI/CD pipelines, infrastructure‑as‑code, automated testing, deployment, monitoring, and release management (e.g., Azure DevOps, Jenkins, Git‑based workflows).

- Deep, hands‑on expertise in the Azure Data Platform, including design, development, and operation of data pipelines and platforms using technologies such as Azure Data Factory, Databricks, Delta Lake, Snowflake, and related Azure services. - Strong experience designing and applying DevOps and DataOps practices for data platforms, including CI/CD pipelines, infrastructure‑as‑code, automated testing, deployment, monitoring, and release management (e.g., Azure DevOps, Jenkins, Git‑based workflows). - Strong understanding of software engineering principles, cloud architecture, information risk, data security, and governance, with the ability to apply these principles pragmatically in complex, enterprise‑scale environments.

- Extensive hands‑on experience in modern programming and scripting languages commonly used in data engineering (e.g., Python, SQL, PySpark; additional languages such as Java or .NET beneficial), with a strong focus on readable, maintainable, and performant code. - Recognised senior‑level engineering expertise in data engineering and cloud platforms, demonstrated through successful ownership of production systems, architectural decision‑making, mentoring of other engineers, and, where applicable, relevant professional certifications (e.g., Azure Data Engineer, Cloud Solution Architect). - Experience working effectively across Agile and hybrid delivery models, including Agile (e.g., Kanban / Scrum) and Waterfall environments, with the ability to influence engineering practices and continuously improve ways of working.

- Strong analytical and problem‑solving skills, with the ability to assess trade‑offs (cost, performance, complexity, risk), make sound technical decisions, and clearly communicate reasoning to both technical and non‑technical stakeholders. - AI‑aware data engineering mindset, with practical understanding of how Azure data platforms enable machine learning and generative AI workloads (e.g. data preparation for ML, feature engineering, model serving integration, and consumption of AI outputs within data solutions). - Experience or working knowledge of Azure AI‑related services (e.g. Azure Machine Learning, Azure OpenAI, Cognitive Services) and how they integrate with Azure Data Platform components, without being responsible for data science or model development.

- AI‑aware data engineering mindset, with practical understanding of how Azure data platforms enable machine learning and generative AI workloads (e.g. data preparation for ML, feature engineering, model serving integration, and consumption of AI outputs within data solutions). - Experience or working knowledge of Azure AI‑related services (e.g. Azure Machine Learning, Azure OpenAI, Cognitive Services) and how they integrate with Azure Data Platform components, without being responsible for data science or model development. In return, we offer you: