Director of Data Engineering

GermanyRemoteFull-timeexecutive

DataData Manager

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director of Data Engineering based in Germany.

Technical Tools

DataData Manager

This is a senior data leadership role responsible for building and scaling the data foundation that powers critical business decisions.
You’ll own the data roadmap, architecture, and company-wide data model while creating a trusted single source of truth for core business information.
The role combines strategic leadership with hands-on technical involvement, from modernizing legacy pipelines to improving data quality, governance, and observability.
You’ll lead a small engineering team, establish clear priorities, and help engineers grow while staying close to the technical details when needed.
As the business scales, you’ll also guide infrastructure and warehouse efficiency, enterprise system integrations, and platform reliability.
AI will be an important part of the team’s operating model, with opportunities to use AI agents for development, incident investigation, and data quality.
The environment is fully remote, high-trust, fast-moving, and focused on building a durable enterprise-level data function from a strong foundation.

Own the data roadmap, architecture, and company-wide data model, establishing a trusted single source of truth for core business data.
Modernize and refactor legacy data jobs, reducing technical debt and creating a platform that can scale with business growth.
Ensure data pipelines remain resilient, reliable, accurate, and consistently available to teams across the organization.
Establish and maintain strong data quality, governance, monitoring, and observability practices.
Manage warehouse and infrastructure costs as data volumes and business requirements increase.
Lead the team's intake and sprint-planning process, prioritizing requests according to business impact while adapting to changing priorities.
Lead, coach, and develop a small data engineering team through code reviews, regular feedback, mentorship, and clear growth paths.
Stay hands-on with technical work when required, contributing directly to architecture, engineering, troubleshooting, and platform improvements.
Integrate new enterprise systems, including ERP platforms, into the data warehouse and broader data ecosystem.
Introduce and scale AI-native ways of working, using AI agents for software development, incident investigation, data quality, and engineering productivity.
Promote a pragmatic engineering approach that starts with the business question and selects the simplest technology capable of solving it.
Foster a culture where data integrity and trust are treated as critical, priorities are managed decisively, and engineers are supported in developing into senior contributors and future managers.

Requirements

~2 min read

8+ years of experience in data architecture or data engineering, including at least 3 years of experience leading teams.
Proven experience building and scaling cloud-based data platforms, with BigQuery or a comparable technology.
Strong hands-on expertise in SQL and Python, along with dbt or a similar data modeling framework.
Deep experience with data modeling and the ability to design data structures starting from business concepts and requirements rather than simply from existing tables.
Experience establishing data quality, governance, monitoring, and observability practices that create confidence across the organization.
Experience managing sprint-based intake, prioritization, and delivery for a data team supporting multiple business stakeholders.
Familiarity with ELT platforms such as Fivetran and comparable data integration tools.
Experience working with enterprise systems and data integrations; ERP experience is a plus.
Experience in direct-to-consumer (DTC) or consumer packaged goods (CPG) environments is preferred.
Strong judgment and prioritization skills, with the ability to balance urgent business needs against long-term platform health and technical debt.
Hands-on leadership style with the ability to move between strategic planning, technical problem-solving, and people management.
Strong coaching and communication skills, with a commitment to developing engineers and creating clear paths for professional growth.
Practical experience using AI agents or AI-assisted engineering tools, with an openness to making AI a meaningful part of day-to-day technical workflows.