Data engineer (вакансія неактивна)

Our client is a Swiss company that operates a digital marketplace for the CNC manufacturing industry, helping businesses find best-fit contractors and conduct transactions safely and efficiently. Their flagship product is the world’s first chat-based intelligence tool for B2B in the industrial sector. It answers specific questions about CNC manufacturing and delivers deep market insights from a specialised database — enabling customers to expand market share, analyse competition, optimise sales, and drive product innovation.

‍Requirements:‍

‍● 5+ years of experience as a Data Engineer (or equivalent hands-on data infrastructure role)
‍● strong SQL and Python skills, with real production experience building and maintaining data pipelines (ETL/ELT)
‍● experience with knowledge graphs, ontology design, or semantic/graph data modeling (Neo4j or similar)
‍● familiarity with a major cloud data stack (e.g., Azure Fabric, Synapse, OneLake, Azure ML, AI Search) or equivalent cloud data platforms
‍● comfortable working with messy, incomplete, or third-party data and building processes to make it usable
‍● good engineering judgment — knowing what data quality bar matters now vs. later, and when “good enough” beats “perfect”
‍● Upper Intermediate English level

Would be a Plus:‍

‍● manufacturing domain experience
‍● experience with agentic tools (Claude Code, Cursor)
‍● exposure to AI/LLM-based products (core of the role is data infrastructure, not agent design)

What you will do:‍

‍● design, build, and maintain data pipelines that ingest, clean, and structure manufacturing data from multiple sources (APIs, crawlers, third-party providers)
‍● build and evolve a knowledge graph (Neo4j): schema design, data modeling, ingestion, and query performance
‍● design and extend the manufacturing ontology — companies, facilities, machines, processes, materials, certifications, capacities — and the relationships between them
‍● own data quality: identify gaps, deduplicate, validate, and monitor pipelines in production
‍● source missing data through third-party integrations, web crawling/scraping, or ML-based inference where direct data isn’t available
‍● extend the data model from one vertical (e.g., CNC machining) to others — injection moulding, sheet metal, additive manufacturing
‍● instrument and track data/pipeline health and downstream agent performance via telemetry tools (LangSmith, PostHog)

Why Rolique?‍

‍● we believe in fairness, transparency and helpfulness in everyday work
‍● your personal development is important to us, therefore we promote the internal transfer of knowledge and strengthen your “zone of genius”
‍● 20 days of paid vacation and 5 days of sick leaves
‍● personal budget for courses, training, and certifications
‍● health support and sports compensation
‍● accounting support