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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - **Company:** TPXimpact Ltd - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Microsoft Access, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Application Frameworks, Microsoft Azure, Big Data, Computer Programming, Continuous Integration, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Warehousing, DevOps, Python (Programming Language), Meta-Data Management, Power BI, SQL Databases, Data Streaming, System Integration Testing, System Testing, Test Data, Data Processing, Google Cloud, Data Strategy, Git, Microsoft Fabric, Pyspark, AWS Data Analytics, Data Management, Software Version Control, Data Pipelines, Databricks - **Published:** June 29, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=311fb097028bef16 ## About the Role Do you have experience in SQL?, Professional knowledge and experience, * 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 warehousing 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 DevOps and CI/CD practices (Git, Azure DevOps 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, * Proficiency in developing and maintaining complex data models (conceptual, logical and physical). * Strong skills in data governance and metadata management. * Experience with data integration design and implementation. * Ability to write efficient, maintainable code for large scale data systems. * Experience with CI/CD pipelines, version control, and infrastructure-as-code (e.g. Git, Azure DevOps). * Strong stakeholder communication skills, with the ability to translate technical concepts into business terms. * Ability to mentor junior engineers, foster collaboration, and build a high-performing data engineering culture. Behaviours and PACT values * Purpose: Be values-driven, recognising that our client's needs are paramount. Approach client engagements with professionalism and creativity, balancing commercial and operational needs. * Accountability: Be accountable for delivering your part of a project on time and under budget and working well with other leaders. Lead by example, promoting a culture where quality and client experience are foremost. * Craft: Balance multiple priorities while leading high-performing teams. Navigate ambiguity and set the technical direction and approach to support positive outcomes. * Togetherness: Collaborate effectively with others across TPXimpact. Build strong relationships with colleagues and clients. ## Description * 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 workstreams (team size 5-15) * Resource complexity: Provides leadership across multiple workstreams 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 (6-12 months), ensuring that near-term work supports broader programme and client objectives., * 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 activities and results for complex data products and support others to do the same ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Software Engineer Salary London](https://www.wearedevelopers.com/magazine/252-software-engineer-salary-london) - [What Are The Top Skills Required For Azure Developers?](https://www.wearedevelopers.com/magazine/77-what-are-the-top-skills-required-for-azure-developers)