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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