Lead Data Engineer

Lead Data Engineer – Kanzlei Ganz Gärtner Lindberg Slania

Job level: 10

We are 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 when it comes to data engineering principles.
• Champion data engineering across projects and clients.

• 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.

• Headcount:
  Typically leads a multidisciplinary team or multiple work streams (team size 5–15).
• 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:
  Works across mid‑ to long‑term delivery cycles (6–12 months), ensuring that near‑term work supports broader programme and client objectives.

• 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 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.

• 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).

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,…