Data Platform Engineer

About the Job

As a key growth accelerator, A11 partners with Europe’s most ambitious startups and scaleups, corporates and SMBs to connect them with top-tier talent ready to make an impact. 

We're looking for a Senior Data Platform Engineer to build the data foundation at one of our Ecosystem Companies. This is the first dedicated data platform hire: you own the platform end to end, work closely with the Head of Data, and lay the groundwork for pricing, forecasting, and AI agents, with a path to leading a small data engineering team.

Join the A11 Ecosystem – a next-generation supply chain platform redefining how one of the world's largest industries sources, distributes, and finances materials. Engineered to unlock the full potential of global trade, it harnesses agentic AI and workflow automation to provide seamless, high-efficiency access to premium materials at scale. Since its commercial rollout, the platform has fueled exponential growth, optimizing procurement for thousands of businesses while setting new benchmarks for speed, transparency, and efficiency. Its competitive edge lies in full digitization and AI-driven supply chain management automation, eliminating inefficiencies and creating a direct, intelligent bridge between suppliers and buyers. With a team of 80+ FTEs across Europe (Berlin HQ), China, and India, the company is rapidly scaling toward profitability, driving triple-digit million GMV, and setting new industry standards in autonomous, AI-powered supply chain orchestration.

In this role, you’ll:

Own the data platform

• Own the lakehouse end to end: ingestion from CRM and operational systems, storage, pipeline architecture, orchestration, and governance
• Add near-real-time processing where the business needs speed, and keep batch where it is sufficient
• Set engineering standards that the team will grow into

Turn data into trusted datasets

• Model commercial and logistics data (quotes, orders, margins, suppliers, shipments) into trusted datasets shared by finance, sales, procurement, and logistics
• Turn business questions into reusable datasets and metrics together with the Head of Data, instead of one-off exports

Build the foundation for pricing, forecasting, and AI agents

• Create features, signals, historical snapshots, and feedback loops that show whether decisions were right
• Prepare the data layer for dynamic pricing and demand sensing

Make data quality visible

• Introduce tests, monitoring, and alerting so pipeline failures and silent schema changes are caught early

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