Data Engineering Manager

Overview

In this role you lead cloud-scale data engineering programs and AI-driven data delivery, shaping data products for AI and analytics. You’ll guide teams through pipeline design, data modelling, and governance, leveraging AI-assisted practices to raise delivery quality. You work within a cross-functional Data & AI team to accelerate cloud migrations and platform enhancements. This is a hands-on leadership role at scale, with a focus on automating data quality and enabling autonomous data workflows in production.

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 programs and platform migrations across major cloud platforms
• Drive AI-assisted pipeline development and establish norms for GenAI tooling in build, testing, and documentation
• Design and implement AI-driven data quality, schema drift detection, and lineage capabilities
• Own end-to-end data product design and governance from requirements to delivery
• Lead ETL/ELT modernization and cloud migration initiatives with AI-assisted approaches
• Architect observability, resiliency and self-healing pipelines with AI-driven monitoring
• Advance agentic data patterns and human-in-the-loop AI operations in pipelines
• Translate modelling patterns into reusable automation frameworks and skills
• Establish CI/CD, testing automation and engineering standards for data delivery
• Mentor engineering teams in AI-augmented data engineering practices
• Contribute to propositions, accelerators and reusable data engineering assets

Key requirements

• Experience designing and delivering cloud-scale data engineering solutions on Azure, Databricks, Microsoft Fabric, Snowflake, AWS or GCP
• Proven AI-assisted pipeline development and governance of AI-generated outputs
• Expertise in data quality, observability, lineage, and automated governance
• Data modelling including dimensional design and medallion architecture with scalable patterns
• ETL/ELT delivery and legacy-to-cloud migration at scale
• Data product thinking from pipeline to consumption
• Agentic AI patterns in data engineering with autonomous or human-in-the-loop workflows
• CI/CD and engineering standards for data platforms with AI-assisted practices
• Experience leading and mentoring engineering teams

• leadership
• mentoring
• collaboration
• Azure
• Databricks
• Microsoft Fabric

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