Data Engineering Manager

Overview

As Data Engineering Manager, you will lead cloud-scale data engineering programs and platform migrations across major cloud platforms, shaping AI-powered pipelines and data products. You’ll drive AI-assisted development, governance and quality to enable reliable AI, analytics and business outcomes. You’ll mentor teams, establish engineering standards and foster a culture of continuous improvement in a global delivery environment. This is a chance to impact how data products are built and used at scale, in a innovative, diverse, client-focused setting.

Pay / Benefits

• 30 days’ vacation per year
• private medical insurance
• 3 extra days’ leave per year for charitable work

Responsibilities

• Lead delivery of cloud-scale data engineering programmes and platform migration across Azure, Databricks, Microsoft Fabric, Snowflake, AWS and GCP
• Drive AI-assisted pipeline development as standard engineering practice, including GenAI tooling for build, testing and documentation
• Design and implement data quality, schema drift detection and lineage using AI-driven automation
• Own data product design and governance end-to-end from consumption requirements to data modelling, pipelines and ongoing governance
• Lead ETL/ELT modernization and cloud migrations with AI-assisted delivery
• Architect observability, resiliency and self-healing pipelines with AI-driven monitoring
• Promote agentic pipeline patterns incorporating autonomous and human-in-the-loop AI operations
• Translate data modelling patterns into reusable agent skills and automation frameworks
• Establish CI/CD, testing automation and engineering standards across data delivery with AI integration
• Mentor engineering teams, set technical standards and foster continuous improvement
• Contribute to proposition development, accelerators and reusable data engineering assets

Key requirements

• Design and delivery of cloud-scale data engineering solutions across Azure, Databricks, Microsoft Fabric, Snowflake, AWS or GCP
• AI-assisted pipeline development with GenAI tooling
• Data quality, observability and automated governance (schema drift, lineage)
• Data modelling including dimensional/Kimball design and medallion architecture, translate to scalable patterns
• ETL/ELT delivery and legacy-to-cloud migration at scale
• Data product thinking with consumption-first mindset
• Agentic AI patterns in data engineering (autonomous or human-in-the-loop)
• CI/CD and engineering standards for data platforms with AI-assisted delivery
• Experience leading and mentoring engineering teams
• Ability to work across cross-functional teams and complex client environments

• Leadership
• Mentoring
• Collaboration
• Azure
• Databricks
• Microsoft Fabric

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