Lead Data Engineer

Join Lead Data Engineer role at Kanzlei Ganz Gärtner Lindberg Slania.

We’re looking for a Lead Data Engineer to join our Data Engineering and Analytics practice.

• 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 for data engineering principles.
• Champion data engineering across projects and clients.

• Lead by example, holding responsibilities for team culture and project impact.
• 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 teams.
• Ensure teams and projects are inclusive through how you lead and manage others.
• Effectively own and hold the story of our work, measuring 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 writing, speaking and presenting.

• Headcount: Typically leads a multidisciplinary team or multiple workstreams (team size 5‑15).
• Resource complexity: Provides leadership across multiple workstreams or technical domains, overseeing junior leads or specialists.
• Problem‑solving responsibility: Solves highly complex problems, balancing technical, user, business, and operational needs.
• Change management: Leads or co‑leads significant change initiatives, managing stakeholder expectations and embedding sustainable ways of working.
• Internal/External interactions: Acts as a trusted partner to client and internal stakeholders, leading workshops and presentations.
• Strategic timeframe: Works across mid‑to‑long term delivery cycles (6‑12 months).

• 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 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).
• Ability to communicate technical concepts to technical and non‑technical audiences.
• Proven experience delivering complex projects in a fast‑paced environment with tight deadlines.

• Advanced knowledge of data governance, data standards and best practices.
• Experience in a consultancy environment, demonstrating flexibility and adaptability.
• Experience defining and enforcing data engineering standards, patterns and reusable frameworks.
• Professional certifications (e.g., Microsoft Azure Data Engineer, AWS Data Analytics, Databricks Certified Professional Data Engineer).

• Design, build and test complex or large‑scale data products.
• Build and lead teams to deliver data integration services and reusable pipelines meeting performance, quality and scalability standards.
• Collaborate with architects to align solutions with enterprise data strategy.
• Work with data analysts, engineers and data science/AI specialists to design and deliver products effectively.
• Understand data cleansing and preparation, implementing reusable processes and checks.
• Optimize data pipelines and queries for performance and cost efficiency.
• Define system integration testing conditions for complex data products, support others, analyze and report test activities.
• Develop and maintain complex data models (conceptual, logical, physical).
• Strong skills in data governance and metadata management.
• Experience with CI/CD pipelines, version control, and infrastructure-as-code (Git, Azure DevOps).
• Strong stakeholder communication, translating technical concepts into business terms.
• Mentor junior engineers and build a high‑performing data engineering culture.

• Purpose – values‑driven, putting client needs first.
• Accountability – deliver on time and under budget, promoting quality and client experience.
• Craft – balance priorities, navigate ambiguity, set technical direction.
• Togetherness – collaborate effectively, build strong relationships.

We’re a purpose‑driven organisation supporting organisations to build a better future for people, places and the planet. We combine experience in the public, private and third sectors with expertise in human‑centred design, data, experience and technology to create sustainable solutions for an ever‑evolving world.

• 30 days holiday + bank holidays
• 2 volunteer days for causes you care about
• Maternity/paternity – 6 months maternity leave, 3 months paternity leave
• Life assurance
• Employer pension contribution of 5%
• Health cash plan
• Personal learning and development budget
• Employee Assistance Programme
• Access to equity via a Share Incentive Plan
• Green incentive programmes (Electric Vehicle Leasing, Cycle to Work Scheme)
• Financial advice
• Health assessments