Data Engineering Manager

Overview

As Data Engineering Manager, you will steer cloud-scale data engineering programs and AI-driven pipelines, shaping data products for AI and analytics. You collaborate across cross-functional teams to deliver scalable, observable, and governance-driven data platforms. You’ll implement AI-augmented approaches and mentor engineers, contributing to reusable assets and competitive propositions. This role offers hands-on leadership in a culture that values innovation, impact, and global collaboration.

Pay / Benefits

• 30 days vacation per year
• private medical insurance
• 3 extra days for charitable work per year
• flexibility to work onsite with clients and partners

Responsibilities

• Lead delivery of cloud-scale data engineering and platform migration across Azure, Databricks, Microsoft Fabric, Snowflake, AWS and GCP
• Drive AI-assisted pipeline development as standard practice using 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 quality end-to-end from requirements to governance
• Lead ETL/ELT modernization and legacy-to-cloud migration at scale
• Architect observability, resiliency, and self-healing pipeline capabilities with AI-driven monitoring
• Promote agentic data workflows with autonomous/human-in-the-loop AI operations
• Translate data modelling patterns into reusable automation/frameworks for scalable delivery
• Establish CI/CD, testing automation and engineering standards across data delivery
• Mentor engineering teams and contribute to reusable accelerators and assets
• Contribute to proposition development and client-facing accelerators

Key requirements

• Cloud-scale data engineering design/delivery across Azure, Databricks, Microsoft Fabric, Snowflake, AWS or GCP
• AI-assisted pipeline development with GenAI tooling
• Data quality, observability, and automated governance including schema drift and lineage
• Data modelling involving dimensional/Kimball design and medallion architecture
• ETL/ELT delivery and legacy-to-cloud migrations
• Data product thinking with consumption-first mindset
• Agentic AI patterns in data pipelines
• CI/CD and engineering standards for data platforms including test automation
• Leadership and mentoring of engineering teams
• Ability to translate patterns into reusable frameworks

• leadership
• mentoring/people development
• cross-functional collaboration
• Azure
• Databricks
• Microsoft Fabric

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