Lead Data Engineer

Lead Data Engineer

Join Lead Data Engineer role at Kanzlei Ganz Gärtner Lindberg Slania
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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 work streams (team size 5‑15).
• Resource complexity:
  Provides leadership across multiple work streams 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 Dev Ops and CI/CD practices (Git, Azure Dev Ops).
• 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 Dev Ops).
• Strong stakeholder communication, translating technical…