# Senior Data Platform Engineer at Andercore > Hybrid · Germany · Senior · Backend · open. Source: https://alderhq.app/jobs/ashby%3Aandercore%3A35d4cf4d-9ce7-447e-884c-ec2e9c701d14 (read 2026-11-09). ## Facts (observed) - Company: [Andercore](https://alderhq.app/companies/andercore) - Location text: Berlin - Where it can be done from (inferred): Hybrid · Germany (evidence: "Location: Berlin") - Posted: 2026-09-28 (41 days ago) - First seen by alder: 2026-11-04 · last verified: 2026-11-07 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/andercore/35d4cf4d-9ce7-447e-884c-ec2e9c701d14 - Skills listed (inferred): Python, Spark, SQL ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (Berlin, n 6): p25 €76k, median €86k, p75 €115k ## Description (the employer's text) ### About Andercore Andercore is the AI-native supplier of industrial materials and goods for energy and infrastructure. We execute trade and distribution end-to-end, on our own account, at the speed of compute - and we are becoming the supplier of record for the entire industry. Industrial procurement is a €900B market in Europe alone and the largest manual market on earth. 95% of customers have no direct access to global supply chains. Materials pass through four or five intermediaries before they reach a site, and 40% of the margin evaporates on the way. For decades, the operating system of this industry was people: people to source, negotiate, finance, ship, and sell. We built the alternative from scratch. We are the supplier. We buy from global sources and sell to European customers on our own account. Our AI runs the full trade: sourcing, quality, pricing, sales, logistics, credit, and financing. One request. A firm quote in minutes. Delivered on time. For the customer, it feels like buying from their preferred local supplier. No warehouses. No fleet. No inventory risk. Every trade is pre-sold before it is bought. We are also the credit counterparty. Customers are scored and insured in minutes and get up to 60 days of payment terms priced into the quote itself. The working capital behind every trade is bank-funded, not equity-funded. Where we are today: Strong triple-digit-million euro turnover run rate, the majority of it from repeat customers Seven European markets live, sourced from suppliers across Asia and beyond 80+ people across Berlin (HQ), London, and Shanghai $90M raised in equity and debt, including our $40M Series B, from Atomico, Project A, Target Global, and Inven Capital - with working capital facilities from UniCredit and KfW The margin is in the bits. The moat is in the atoms. We are building the supplier of record for industrial materials - and redefining how global trade works in one of the largest and most essential industries on earth. ### Why this role, why now We already run on Databricks, and we have a Head of Data who owns metrics, KPIs and business analytics. What's missing is the engineer who makes the platform solid: clean ingestion, a trustworthy model of our commercial and logistics domain, and a foundation ready for the next step, which is dynamic pricing and demand sensing. You'll be the first dedicated data platform hire. You'll set the standards the team grows into, with a path to leading a small data engineering team as we scale. ### 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aandercore%3A35d4cf4d-9ce7-447e-884c-ec2e9c701d14 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.