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Salary: EUR 90,000 - 140,000 / annual
Langdock is an AI platform used by over 10,000 European enterprises to provide secure access to leading AI models, build and share agents, and automate workflows. The company has grown past $40M ARR while maintaining a lean, efficient team.
As the first dedicated data hire, you will own the complete data system from raw data collection through trusted business decisions. You will sit between product and engineering, combining data engineering, analytics engineering, product analytics, and technical platform ownership. The company already has foundations in place—dbt transformations on ClickHouse, ingestion of product/billing/customer data, and Metabase dashboards—and your role is to evolve these into a reliable, trustworthy system.
Key responsibilities include:
- Define and model company metrics to ensure consistency and clarity across teams
- Build a reliable model of product usage, translating raw application data into insights on activation, adoption, retention, feature usage, and customer health
- Own the data platform in production: build and operate ingestion pipelines, warehouse models, data quality checks, deployment workflows, and monitoring
- Partner with product squads to understand user behavior, answer exploratory questions, and evaluate product changes
- Improve product instrumentation by identifying data blind spots and designing better events and source data
- Enable Account Management, Revenue, Finance, and leadership with dependable reporting and analysis
- Create self-service foundations through clear marts, documentation, and dashboards
This role differs from pure analytics or pure data engineering—you own the complete system and must develop deep product and business context to model ambiguous data correctly while maintaining technical reliability as the product evolves quickly. You will establish standards for data modeling, quality, ownership, observability, and collaboration across the organization.
Success in the first year means product teams use consistent metric definitions, squads can understand adoption without questioning data accuracy, important pipelines have explicit owners and monitoring, instrumentation gaps are addressed during product development, recurring questions are answered through reusable models, and the company has a clear technical direction for platform evolution.
The role is in-office at Greifswalder Strasse 212 in Berlin. The company works together in person to build trust, develop shared context, and make decisions quickly. Most engineers start around 8:30, with lunch together and dinner available for those staying later.
REQUIREMENTS:
- Built and operated production data pipelines and analytical models (not only consumed prepared data)
- Ability to move between infrastructure, SQL modeling, exploratory analysis, and conversations about product/business definitions
- Treat data quality and metric definitions as product responsibilities
- Comfortable with dbt, analytical databases, deployment systems, and business intelligence tools
- Investigate surprising numbers until understanding underlying product behavior and technical mechanisms
- Turn ambiguous questions into clear definitions, dependable models, and actionable explanations
- Motivated by broad ownership and establishing foundations for a growing data function
- Existing right to work in Germany (no visa sponsorship available)
