Engineering Manager - Data Platform (m/f/d)

Description

The company runs a platform on AWS that lets teams across the company work with data on their own. It follows Data Mesh principles and provides the infrastructure, self-service tools and governance that domain teams use to own and manage their data products.

The Data Platform Engineering team builds and runs this platform. As Engineering Manager, you lead the team, combining people leadership with hands-on technical work on architecture, hard engineering problems, reliability, performance and cost.

The company is also building AI into how its Data Engineering organization works and how people use the platform. That means putting AI into the daily work of Data Engineers and Analytics Engineers, and making the platform easier to use for business users.

Responsibilities

• Grow a team that owns its work, makes sound technical calls and delivers without needing close supervision.
• Shape the AWS data platform, deciding on architecture, reliability, performance, security and cost.
• Pair with engineers on hard technical problems, coding yourself when needed.
• Build and extend self-service features for pipelines, dataset discovery, access management and data quality monitoring.
• Push the Data Mesh setup forward, covering domain ownership, data contracts, access controls and federated governance.
• Raise data quality, lineage and observability through automation and controls built into the platform.
• Partner with Product, Analytics Engineering, Data Governance and business stakeholders to understand their problems and design solutions.
• Bring AI into the engineering tools Data and Analytics Engineers already use, to help with pipeline development, data modelling, testing, code review and troubleshooting.
• Build AI features into the platform so users can find and understand datasets, query data in natural language, investigate issues and handle tasks that currently need technical skills or manual work.

Requirements

• 5+ years as an Engineering Manager, with a proven record of hiring, coaching and growing engineers.
• A solid software or data engineering background, with recent hands-on work and the ability to weigh in on complex engineering choices.
• Extensive daily use of AI development tools such as Claude Code, Codex or Cursor, going beyond basic code generation.
• Proven experience putting AI into engineering workflows across the SDLC, ideally for Data or Analytics Engineering teams, with solutions engineers actually use and measurable efficiency gains.
• Experience building AI into internal platforms or products, not only standalone chatbots. You know how to connect AI with existing tools, data, metadata, permissions and workflows.
• A product mindset: you grasp business and user needs, challenge proposed solutions and work well with Product and other stakeholders.
• Deep AWS expertise in building and operating production data platforms with S3, Glue, Athena, IAM, MWAA and Lake Formation.
• A strong grasp of data lake and lakehouse architecture, covering storage, processing, metadata, security, performance and cost.
• Experience running large-scale data platforms, fixing production issues and improving reliability and efficiency.
• Proficiency in Python and SQL, plus practical skills in infrastructure as code, CI/CD, testing and engineering best practice.
• Hands-on experience with Data Mesh principles, decentralized data ownership, self-service data infrastructure and federated governance.
• Experience implementing data quality, data contracts, observability and access controls in production data platforms.
• Strong communication and people leadership skills, including developing engineers, delegating responsibility and building an effective, independent team.

Conditions

Equal opportunity: people from all backgrounds are welcome, and the company works to help everyone feel included.

Benefits

• Tools, training and support so you can work in the way that suits you, learn new skills and reach your potential.
• A culture that supports growth.