Langdock is the AI platform used by more than 10,000 companies to give employees secure access to the leading AI models, to build and share agents and to automate repetitive workflows. We have grown past $40M ARR while remaining a small team, and we care deeply about operating efficiently across the entire company.
For many enterprises, Langdock is becoming the place where most of the net-new work is produced. As people and agents create more documents, analyses, decisions, and automations inside AI interfaces, the context and data behind that work accumulate within Langdock. This gives us the opportunity to earn a larger role in their technology stack by building a platform they choose to rely on.
Our ambition is to build that platform for European enterprises while preserving their control over data, model providers, and deployment environments. We have made meaningful progress at the application layer, but much of the foundation beneath it still needs to be built.
You can watch the Meet the engineering team video to get a feeling for how we work.
Data at Langdock sits close to Product Development because our product is our core business. Understanding how people use Langdock requires detailed product context, especially as the product evolves quickly and raw events can be easy to misinterpret.
As our first dedicated data hire, you will own the path from raw data to trusted decisions. You will sit between product and engineering, combining data engineering, analytics engineering, product analytics, and technical platform ownership.
You will not start from scratch. We already run the dbt transformation framework on ClickHouse, ingest product, billing, and customer data, and provide business-facing marts and Metabase dashboards. Your role is to turn these foundations into a reliable data system that the company can trust and build on.
Make company metrics trustworthy. Define and model metrics so people understand what each number means, how it is calculated, and where its limitations are. Resolve inconsistencies and prevent teams from working with conflicting definitions.
Build a reliable model of product usage. Develop a deep understanding of Langdock's products and translate raw application data into useful models for 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. Ensure that data remains accurate, fresh, observable, and available when people depend on it.
Partner with Product Engineers. Help product squads understand how people use what they build, answer exploratory questions, evaluate product changes, and identify opportunities or problems that are not obvious from individual customer conversations.
Improve product instrumentation. Find blind spots in the data we collect and work with engineers to design events and source data that make future analysis reliable.
Serve the company's core data consumers. Enable Account Management to understand adoption within specific accounts, help Revenue and Finance understand where the company makes money, and provide leadership with dependable company-level reporting.
Create self-service foundations. Build clear marts, documentation, and dashboards that let teams answer recurring questions without requiring a custom analysis each time.
This is neither a dashboard-only analytics role nor an infrastructure-only data engineering role. You will own the complete system, from how product data is generated to how a metric is interpreted in a product or business decision.
The hardest part is not writing SQL or operating ClickHouse. It is developing enough product and business context to model ambiguous data correctly while building technical systems that remain dependable as Langdock changes quickly.
Because this is our first dedicated data hire, you will establish the standards for data modeling, quality, ownership, observability, and collaboration across Product Development.
Within your first year:
Product, Account Management, RevOps, and leadership use consistent definitions for the company's most important metrics.
Product squads can understand adoption and behavior without questioning whether the underlying data is correct.
Important pipelines and models have explicit owners, tests, freshness expectations, monitoring, and recovery procedures.
Gaps in product instrumentation are identified and addressed as part of product development.
Recurring questions are answered through reusable models and dashboards rather than one-off queries.
The company has a clear technical direction for how the data platform should evolve.
You have built and operated production data pipelines and analytical models, not only consumed data prepared by others.
You can move between infrastructure, SQL modeling, exploratory analysis, and conversations about product or business definitions.
You treat data quality and metric definitions as product responsibilities.
You are comfortable working with dbt, analytical databases, deployment systems, and business intelligence tools.
You investigate surprising numbers until you understand the underlying product behavior and technical mechanism.
You can turn an ambiguous question into a clear definition, a dependable model, and an actionable explanation.
You want broad ownership and are motivated by establishing the foundations for a growing data function.
This is an in-office role at Greifswalder Strasse 212 in Berlin. We work together in person because it helps us build trust, develop shared context, and make decisions quickly.
Most engineers start around 8:30. We usually eat lunch together, and dinner is available for people who stay later. Running and gym are part of the routine for many of us.
You need an existing right to work in Germany. We do not currently sponsor visas.
The salary range for this role is €90,000–€140,000 gross per year, depending on level and scope. All roles include equity. Salaries are tied to levels, not negotiation.
We will figure out the right level together based on your experience and scope. Levels are about the work you own, not your title or years of experience.
