What you will do

• Own our Databricks lakehouse end to end: ingestion from Salesforce and our operational systems, storage, pipeline architecture, orchestration and governance
• Model the commercial and logistics domain (quotes, orders, margins, suppliers, shipments) into clean, trusted datasets that finance, sales, procurement and logistics can all use without keeping their own version of the truth
• Work closely with our Head of Data to turn business questions into reusable datasets and metrics, not one-off exports
• Build the data foundation for pricing, forecasting and our AI agents: features, signals, historical snapshots and feedback loops that tell us whether a decision was right
• Make data quality observable: tests, monitoring and alerting, so broken pipelines and silent schema drift get caught before a buyer notices
• Add near-real-time processing where the business needs to act now, and keep things simple where batch is enough

Who you are

• 5+ years in data engineering, with real ownership of a platform rather than a single pipeline
• Deep SQL and Python, plus hands-on experience with Spark and a lakehouse (Databricks strongly preferred: Delta Lake, Workflows, Unity Catalog)
• You've used a CRM or ERP as a primary source system and know what it means to model messy operational data that people edit by hand
• Comfortable with infrastructure as code and with software engineering practices for data (version control, CI, testing)
• You talk to non-technical stakeholders easily and push back when a request should be solved differently
• You work well in a Series B setting where priorities shift and nobody hands you a finished spec
• You use AI coding tools as a natural part of your work and know where they help and where they don't

Nice to have

• Streaming experience (Kafka, Kinesis or equivalent)
• Machine learning and statistical analysis, especially applied to forecasting or pricing
• Salesforce
• Experience in commerce, marketplaces or supply chain, and an interest in catalog, pricing and fulfilment problems
• A degree in Computer Science, Engineering or a related field

What we offer

• A growing 12-person product and engineering team where you own your area end to end
• A company where data work is not a support function: our agents and our margin depend on it directly
• Hybrid setup in Berlin
• Direct access to the founders and to the commercial teams whose problems you are solving