Ontology Engineer

Andersen is hiring an Ontology Engineer for a project building a large scale Knowledge Graph and delivering AI-powered data and identity solutions. 

The company is a global technology provider delivering data analytics and AI-powered solutions that help organizations better understand customer behavior, optimize marketing performance, and make informed business decisions. By transforming large volumes of data into actionable insights, it enables enterprises to improve audience engagement, measure the effectiveness of their initiatives, and enhance decision-making. The company works with a diverse range of customers, supporting them in navigating an increasingly data-driven and rapidly evolving digital landscape. 

The project is focused on building a large-scale Knowledge Graph and Identity platform that models relationships between audiences, content, brands, devices, and behavioral data. It combines graph technologies, big data processing, and AI-powered enrichment to support identity resolution, audience intelligence, measurement, and advanced analytics. 

Responsibilities: 

- Implementing and extending RDF/RDFS/OWL ontology schemas in the graph database — adding entity classes, properties, and constraints in a consistent, governed way under the direction of the Senior Ontologist. 
- Building and maintaining SHACL validation shapes for post-load graph consistency checks; identify and triage data quality and schema violations. 
- Supporting ontology versioning, changelog documentation, and consistency checking across schema updates. 
- Writing efficient, well-structured SPARQL queries and graph traversals to support downstream data science and product use cases. 
- Contributing to the event-to-ontology transformation and derivation layer — building PySpark/Databricks pipelines that aggregate raw TV viewership and web activity events into durable graph attributes (genre affinity, brand affinity, topic affinity, viewing summaries, lifecycle signals). 
- Implementing derivation logic specified by the Senior Ontologist and data science team; validating outputs against SHACL shapes before graph load. 
- Supporting incremental refresh and updating logic aligned with the graph's batch refresh cadence. 
- Writing production-quality Python — clean, well-tested, documented, and reusable by teammates. 
- Working with PySpark and Databricks to process and transform high-volume data as part of graph pipeline development. 
- Applying embedding-based approaches (semantic similarity, vector search) to entity matching and ontology alignment tasks. 
- Contributing to team tooling, documentation, and reusable components that improve knowledge graph development efficiency. 
- Partnering closely with data engineering on pipeline design, data quality, and incremental ingestion patterns feeding the materialized graph substrate. 
- Participating in ontology design reviews and cross-functional working groups. 
- Working with product and operations teams to understand use case requirements and translate them into graph schema updates. 
- Actively developing expertise in W3C semantic web standards, RDF-native graph databases, and entity resolution under the guidance of the Senior Ontologist. 

Must-haves: 

- Hands-on experience in knowledge graph development, semantic data modeling, ontology engineering, or a closely related field for 2+ years. 
- Working knowledge of W3C semantic web standards: RDF, RDFS, OWL, and SPARQL — with practical experience querying or building in at least one triplestore or graph database. 
- Familiarity with SHACL or equivalent constraint and validation frameworks for graph data quality. 
- Strong Python skills — clean, readable, production-quality code with testing and documentation. 
- Solid understanding of data modeling fundamentals — entity-relationship design, taxonomies, hierarchies, and how to represent complex real-world relationships in structured form. 
- Familiarity with entity resolution or data matching concepts — understanding of why the same real-world entity appears under different identifiers across data sources. 
- Bachelor's degree required in Computer Science, Information Science, Mathematics, or a related field. 
- Detail-oriented and proactive about flagging data quality issues and schema inconsistencies. 
- Level of English – from Intermediate+ and above. 

Nice-to-haves: 

- Hands-on experience with Amazon Neptune or Stardog - or equivalent RDF-native triplestore; exposure to data virtualization (Neptune Orion or Stardog Virtual Graphs). 

- Working knowledge of PySpark and Databricks — particularly for large-scale event aggregation and transformation pipelines. 

- Familiarity with embedding models, vector search, or semantic similarity — applied to entity matching, ontology alignment, or knowledge graph enrichment. 

- Experience with LLM APIs or RAG-based approaches applied to information extraction, entity disambiguation, or schema mapping. 

- Domain knowledge in media, entertainment, or ad tech — content metadata, advertising entities, TV viewership data, or audience/identity data. 

- Exposure to identity resolution, probabilistic record linkage, or device graph approaches. 

Reasons why this job would be interesting to you: 

- Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc.. 

- The opportunity to change the project and/or develop expertise in an interesting business domain. 

- Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee. 

- The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities. 

- Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated. 

- Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies). 

- Certification compensation (AWS, PMP, etc). 

- Referral program. 

- Private health insurance and sports compensation, depending on the type of employment. 

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