{"id":2112372,"url":"https://alion.io/job/bayer-senior-knowledge-engineer-2","title":"Senior Knowledge Engineer","company":{"id":2189753,"name":"Bayer Polska","domain":"bayer.com.pl","url":"https://alion.io/company/bayer-com-pl","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":20240,"max":25300,"currency":"PLN","period":"month","gross":null,"usd_annual":77676},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Git","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Databricks","optional":true},{"name":"ElasticSearch","optional":true},{"name":"GCP","optional":true},{"name":"GraphDB","optional":true},{"name":"GraphRAG","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"Neo4j","optional":true},{"name":"RAG","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-09-25T13:21:05Z","employer_posted_date":null,"last_verified_at":"2026-09-25T13:21:05Z","board_verified":false,"closed_at":null,"days_open":21,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":21},"description":"For Digital Hub Warsaw, we are looking for: \nSenior Knowledge Engineer \nAs a Senior Knowledge Engineer, you will join Bayer's Data & AI team and help build data products and knowledge-driven solutions that enable analytics, AI, and scientific innovation across the organization.\nYou will work at the intersection of Data Architecture, Knowledge Engineering, and AI Enablement, helping to create scalable, AI-ready data foundations that support business users, scientists, analysts, and intelligent applications.\nYou will collaborate closely with domain experts, data engineers, data scientists, and product teams to model knowledge, connect data across systems, and design semantic and graph-based solutions that unlock the value of enterprise data.\n\nWhat We Are Looking For\nWe welcome candidates from diverse backgrounds including:\nData Architecture\nAI/Data Architecture\nKnowledge Graph Engineering\nSemantic Engineering\nData Engineering\nData Governance\nPlatform Engineering\nWe value problem-solving, architectural thinking, and the ability to build scalable AI-ready data solutions over expertise in any single technology or framework.\nZakres obowiązków\nDesign and implement conceptual, logical, and physical data models.\nDevelop semantic layers and knowledge representations that improve data discoverability and reusability.\nBuild and evolve knowledge graph solutions, metadata-driven architectures, and semantic data products.\nCollaborate with domain experts to capture and formalize business knowledge into scalable data models.\nEnsure consistency, quality, and usability of enterprise data assets.\nDesign scalable integration patterns across diverse data sources and platforms.\nDevelop data-oriented APIs and services that support analytical and AI-driven use cases.\nEnable interoperability between systems, applications, and intelligent agents.\nContribute to AI-ready data architecture and semantic foundations for GenAI and Agentic AI use cases.\nSupport implementation of knowledge retrieval and graph-based AI solutions.\nEvaluate emerging technologies in Data Management, Knowledge Engineering, Semantic AI, and Generative AI.\nCollaborate with engineering teams through code reviews and architecture discussions.\nPromote best practices related to software development, data engineering, and platform architecture.\nContribute to continuous improvement of engineering standards and delivery practices\nSupport enterprise-wide data governance, metadata management, and data quality initiatives.\nDrive alignment between data architecture decisions and business objectives.\nPromote modern practices related to data sharing, accessibility, and responsible data usage\nDesign solutions that protect enterprise data, ensuring confidentiality, integrity, and availability.\nCollaborate with security and governance teams to ensure compliance with relevant regulations and standards.\nWymagania\nQualifications\n\nRequired: \nBachelor's degree in Computer Science, Information Systems, Data Engineering, Knowledge Engineering, Computational Linguistics, or a related field, or equivalent practical experience.\nStrong experience in data modelling (conceptual, logical, and physical).\nExperience in Data Architecture, Knowledge Engineering, Data Engineering, Metadata Management, or closely related disciplines.\nExperience with knowledge graphs, graph databases, semantic technologies, or metadata-driven architectures.\nHands-on experience designing APIs and data integration solutions.\nStrong engineering background in data engineering, software engineering, cloud engineering, or platform engineering.\nProficiency in at least one programming language.\nFamiliarity with Git and collaborative software development practices.\nStrong analytical, communication, and stakeholder management skills.\nAbility to collaborate effectively across technical and business teams.\n\nPreferred \nExperience with graph database technologies such as Neo4j, Stardog, GraphDB, Amazon Neptune, TigerGraph, or similar platforms.\nExperience with ontology modelling, taxonomies, semantic modelling, or Semantic Web standards (e.g. RDF, OWL, SHACL, SPARQL).\nExperience with modern cloud data platforms such as Databricks, Snowflake, Microsoft Fabric, AWS, Azure, or GCP.\nFamiliarity with metadata management and data governance solutions\nExperience designing AI-ready architectures, knowledge retrieval solutions, GraphRAG, RAG, or Agentic AI applications.\nUnderstanding of FAIR Data Principles, Linked Data concepts, Data Mesh, or comparable modern data architecture approaches.\nFamiliarity with Elasticsearch or enterprise search technologies.\nExperience with CI/CD practices and DevOps tooling.\nKnowledge of data privacy, security, and regulatory requirements.\nExperience within Life Sciences, Healthcare, Pharmaceutical, or Research environments.\nTech 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