Data Engineer - fully remote within Europe (m/f/d)

Role overview

This is a core data engineering role on a Business Intelligence team, focused on owning the operational reliability of an existing on-prem data platform end-to-end. The work splits roughly evenly between keeping production systems healthy and driving meaningful improvements, with AI-assisted tooling woven into the daily workflow. It suits an experienced engineer who can operate independently, validate AI-generated output, and treat data quality as a craft.

Responsibilities

- Investigate and resolve recurring issues in an on-prem Microsoft SQL Server environment, including cross-system access through linked servers - Own the operational health of the data platform: monitor pipelines, catch problems before they become incidents, and carry issues through to resolution without waiting for a ticket - Improve the platform incrementally by modelling new source domains correctly, introducing quality gates where none exist, and optimising slow queries - Use AI tools such as Claude Code, MCP servers, and other LLM-based tooling for BI work, internal tooling, and data pre-processing, then rigorously validate what comes out - Work in Git and ship changes through GitLab CI/CD with peer review - Communicate clearly with data owners across the business about feed quality and corrective action

Requirements

- Significant hands-on experience operating Microsoft SQL Server in production, including linked servers and cross-system access - Strong SQL and Python skills, with the ability to read logs, trace failures, and debug pipelines independently - Comfort using AI-assisted development tools as part of daily work, paired with a disciplined approach to validating generated SQL, code, and numbers against the source of truth - Working knowledge of Git-based version control and CI/CD pipelines such as GitLab - A data-quality mindset and the confidence to flag incorrect feeds directly with data owners - Clear written and spoken English sufficient to explain technical findings to non-engineering stakeholders

Nice to have

- Experience with Microsoft Fabric, including Lakehouse or Warehouse, Data Factory pipelines, or OneLake - Background building custom MCP servers or internal LLM tooling rather than only consuming them - Familiarity with Power BI or another BI tool as a consumption layer - Exposure to fast-growing B2C or subscription-driven business environments

Benefits and work setup

- Fully remote within Europe, with periodic EU meet-ups and an annual company-wide summer gathering - High autonomy and direct ownership of the operational data layer - A team that uses AI tools in real production work and expects engineers to challenge outputs that fall short - Collaboration with BI, analytics, and data-owning teams across the organisation