Lead Data Engineer

About

The Role Job level: 10 Were looking for a Lead Data Engineer to join our Data Engineering and Analytics practice. In this role, you will:
Lead the design, development, management and optimisation of data pipelines to ensure efficient data flows, recognising and sharing opportunities to reuse data flows where possible. Coordinate teams and set best practices and standards when it comes to data engineering principles. Champion data engineering across projects and clients. Responsibilities Lead by example, holding responsibilities for team culture, and how projects deliver the most impact and value to our clients.

Be accountable for the strategic direction, delivery and growth of our work. Lead teams, strands of work and outcomes, owning commercial responsibilities. Hold and manage uncertainty and ambiguity on behalf of clients and our teams. Ensure teams and projects are inclusive through how you lead and manage others. Effectively own and hold the story of our work, ensuring we measure progress against client goals and our DT missions.

Work with our teams to influence and own how we deliver more value to clients, working with time and budget constraints. Strategically plan the overall project and apply methods and approaches. Demonstrably share work with wider audiences. Elevate ideas through how you write, speak and present. Dimensions Headcount :
Typically leads a multidisciplinary team or multiple work streams (team size 515) Resource complexity:
Provides leadership across multiple work streams or technical domains within a project or programme. Responsible for delivery coordination, prioritisation, and quality, often overseeing more junior leads or specialists. Problem-solving responsibility :
Solves highly complex problems, balancing technical, user, business, and operational needs. Applies expert judgement to make decisions, manage risks, and guide teams through ambiguity. Change management requirements:
Leads or co-leads significant change initiatives. Responsible for managing stakeholder expectations, supporting adoption, and embedding sustainable ways of working. Internal/External interactions:
Acts as a trusted partner to client and internal stakeholders at multiple levels. Leads workshops, presentations, and stakeholder engagement to ensure buy-in, alignment, and delivery clarity. Strategic timeframe working towards:
Works across mid- to long-term delivery cycles (612 months), ensuring that near-term work supports broader programme and client objectives. About You Professional knowledge and experience Essential Proven experience in data engineering, data integration and data modelling Expertise with cloud platforms (e.g. AWS, Azure, GCP) Expertise with modern cloud data platforms (e.g. Microsoft Fabric, Databricks) Expertise with multiple data analytics tools (e.g. Power BI) Deep understanding of data wareho using concepts, ETL/ELT pipelines and dimensional modelling Proficiency in advanced programming languages (Python/PySpark, SQL) Experience in data pipeline orchestration (e.g. Airflow, Data Factory) Familiarity with Dev Ops and CI/CD practices (Git, Azure Dev Ops etc) Ability to communicate technical concepts to both technical and non-technical audiences Proven experience in delivery of complex projects in a fast paced environment with tight deadlines Desirable Advanced knowledge of data governance, data standards and best practices.

Experience in a consultancy environment, demonstrating flexibility and adaptability to client needs. Experience defining and enforcing data engineering standards, patterns, and reusable frameworks Professional certifications in relevant technologies (e.g. Microsoft Azure Data Engineer, AWS Data Analytics, Databricks Certified Professional Data Engineer) Skills Data Development Process Design, build and test data products that are complex or large scale Build and lead teams to complete data integration services integration and reusable pipelines that meet performance, quality and scalability standards Collaborate with architects to align solutions with enterprise data strategy and target architectures Data Engineering and Manipulation Work with data analysts, engineers and data science and AI specialists to design and deliver products into the organisation effectively.

Understand the reasons for cleansing and preparing data before including it in data products and can put reusable processes and checks in place. Access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation. Optimise data pipelines and queries for performance and cost efficiency in distributed environments Testing (Data) Review requirements and specifications, and define system integration testing conditions for complex data products and support others to do the same Identify and manage issues and risks associated with complex data products and support others to do the same Analyse and report system test…