Principal Data Engineer

Freshfields Bruckhaus Deringer
Manchester, UK
5 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Artificial Intelligence Airflow Microsoft Azure Continuous Integration Data Validation Information Engineering Data Governance Extract Transform Load (ETL) Data Security Data Warehousing Azure Data Lake SQL Databases
+11 more
Data Streaming Data Logging Azure Data Factory Apache Spark Git Data Lakes Pyspark Data Lineage Collibra Data Pipelines Databricks

Job description

This is a genuine build-and-lead opportunity - architecting a modern, cloud-first data platform from a position of real influence, while growing and shaping the team that delivers it. You will work across a global, cross-functional community, with the scope to drive data democratisation, governance maturity and AI-readiness across the firm.

What you will be doing

  • Design and maintain scalable, secure data lake and lakehouse solutions using Azure Data Lake, Databricks and Delta Lake - built to support real-time insight, advanced analytics and AI/ML at enterprise scale.
  • Develop and optimise batch and streaming ETL/ELT pipelines using Azure Data Factory, PySpark, SQL, Apache Spark and Airflow, integrated with modern tooling like dbt and Git-based CI/CD.
  • Implement data cataloguing, lineage tracking and governance frameworks through Collibra, ensuring data across the enterprise is discoverable, well-documented and compliant.
  • Own the day-to-day leadership, coaching and development of the Data Engineering team within the Global Data Office - supporting recruitment, performance management, skills growth and the adoption of engineering best practices.
  • Work closely with Data Office leadership to define engineering strategy, support delivery planning, and build a high-performing, collaborative engineering culture across a geographically dispersed team.
  • Monitor and tune data jobs for performance, scalability and cost; embed data validation, logging and monitoring to catch issues early and keep the platform reliable.

Requirements

  • Experience in data engineering, including designing and delivering data lake or lakehouse architectures in an enterprise cloud environment, ideally Microsoft Azure.
  • Hands-on expertise with Databricks, Spark, PySpark, SQL and core Azure Data Services (Data Lake Storage, Synapse Analytics, Data Factory).
  • Experience with data governance and cataloguing tools such as Collibra or -Purview, plus familiarity with the modern data stack (dbt, CI/CD, streaming frameworks).
  • Genuine leadership experience, with a passion for developing people as much as building systems.
  • Strong communication skills across technical and non-technical audiences, and a resilient, proactive mindset suited to a fast-paced, evolving environment.

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