AI Data Engineer

About QoreloQorelo uses AI to automate some of the most mission critical work in the enterprise: the management of SAP systems. SAP runs the core of the world's largest companies, including finance, supply chain, and operations, yet keeping it modern is slow, manual, and consultant heavy. We're changing that.We're moving at lightning speed to revolutionize how enterprises run their IT stack, starting with SAP, the heart of many enterprises. Without a strong base system, innovation can never truly succeed. Our AI agents help teams integrate, migrate, and modernize SAP faster and with fewer errors, compressing work that used to take months. We've raised $3.5M from HPI Ventures, the fund tied to the institute of SAP co-founder Hasso Plattner, along with many other great backers. We already work with Fortune 500 and DAX 40 companies, including Mercedes-Benz.Our promiseOur team is made up of true innovators, builders, and entrepreneurs. What we offer is an environment to grow, with real ownership of not just your day to day work but the direction of the company itself. We're looking for people who want to shape a company, not just join one.What You'll OwnDesign and build the data layer that maps relationships across SAP projects: configurations, outcomes, domain patternsOwn our metadata architecture, including how data is labelled, linked, versioned, and made queryableBuild pipelines that ingest SAP telemetry and consulting learnings and output clean, structured knowledgeWork with the AI team to feed the right signals into the modelsImprove the system with every customer and every deploymentWhat You NeedStrong data engineering background across pipelines, schema design, and data modellingExperience with knowledge graphs, graph databases, or relational modelling of complex domain dataComfortable with large, messy, semi structured data from enterprise systemsSystems level thinker who designs architecture first, not just query by queryHigh standards for data qualityNice to HaveFamiliarity with SAP data models or ERP data structuresExperience with embedding pipelines, vector stores, or LLM data infrastructure