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Job Description

Location: London, Watford or Bristol (Hybrid working options available)

The Lead Data Engineer will provide hands-on technical leadership in Azure cloud and Databricks-based solutions within our Enterprise Data Platform. The role requires strong expertise in Azure cloud services, Databricks, data engineering, and DevOps, leading a cross-functional team to build, deploy, and support high-performance data-driven solutions.

The role involves:

Interpreting Outcomes and user stories and translating them into technical solutions. Creating innovative solution designs for domain and enterprise data products. Overseeing Data Analysts to support detailed data discovery. Overseeing data modelling for Finance and Enterprise data products. Leading Product Increment planning to break down solutions into Features and Epics for incremental delivery. Designing and implementing scalable data solutions on Azure and Databricks within the assigned domain. Ensuring appropriate engineering standards are applied to maintain data quality, performance and reliability.

Duties/Responsibilities

Work with Product Owners and Business Analysts to understand Outcomes, refine user stories. Lead solution design for Finance and Enterprise data products, ensuring alignment with enterprise patterns and guardrails. Direct and collaborate with Data Analysts on detailed data discovery, source understanding and requirements refinement. Oversee logical and physical data modelling for Finance and Enterprise data products, working closely with architecture where required. Implement and maintain data pipelines and ETL workflows in Databricks (PySpark, Delta Lake). Contribute to CI/CD pipelines for data applications using Azure DevOps and infrastructure-as-code (Terraform) in line with established patterns. Apply security, access control and compliance standards for Azure and Databricks in collaboration with platform and security teams. Support monitoring, logging and basic cost optimisation for the team’s data products. Support the development of DevOps practices within the team, including reducing technical debt and improving automation over time.

Skills, Knowledge and Experience

Lead Data Engineers are expected to have strong capability in at least three of the following areas of engineering practice.

Core skills

Automation including testing of data pipelines and data products. Strong teamwork, communication and problem-solving skills to collaborate effectively with cross-functional teams. Awareness of security principles and best practices to ensure secure data solutions. Commitment to continuous learning and staying current with Azure, Databricks and data engineering trends. Strong experience working within an agile development methodology, ideally Scaled Agile (SAFe or similar). Excellent time and self-management through effective planning and prioritisation of tasks. Proven and demonstrable data engineering capability. Ability to influence within the team and communicate clearly with technical and non-technical stakeholders.

Data Engineer (New Technology / Microsoft)

Strong experience with Databricks (Spark, PySpark, Delta Lake, and Unity Catalog advantageous). Proficiency in Azure data services (Azure Data Factory, Data Lake, Azure Functions advantageous). Experience contributing to CI/CD pipelines (Azure DevOps, GitHub Actions, Terraform). Scripting and programming skills (Python advantageous). Good understanding of DevOps and automation concepts (e.g. YAML pipelines, IaC). Solid understanding of cloud security, compliance and governance principles. Experience working with Databricks and Azure in a product or Scaled Agile delivery environment.

Qualifications

Degree level IT or technical/scientific subject (or equivalent experience). Microsoft Azure Data Engineer or Solutions Architect certification (desirable). Databricks Certified Data Engineer or Machine Learning Associate (desirable). Experience with streaming solutions (Kafka, Event Hubs, Spark Streaming) (desirable). Knowledge of machine learning and AI on Databricks (desirable).

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