(Senior) Data Engineer (f/m/d): SAP Data on RDF Knowledge Graphs

What you’ll do

At SAP, we’re integrating state‑of‑the‑art AI technology with our industry‑specific data and deep process knowledge to innovate across SAP products & services. This role focuses on building the data foundation for SAP’s Knowledge Graphs, addressing challenges of structured and unstructured business data, and enabling generative AI solutions.

The Role

• Unique opportunity to build the data foundation for SAP’s Knowledge Graphs – the source of explicit knowledge across multiple SAP domains.
• Design and build robust data pipelines that ingest, transform, and load structured and unstructured business data (including SAP data) into RDF Knowledge Graphs at scale.
• Map source data into ontologies and SHACL schemas, develop SPARQL‑based transformations, and operate triple stores in production alongside Knowledge and Data engineering teams and stakeholders.
• Collaborate closely with domain specialists and data owners to understand source systems (SAP and non‑SAP) and translate their requirements into reliable Knowledge Graph data flows and provisioning.
• Make critical design decisions on the data engineering stack – triple stores, ETL/ELT tooling, orchestration, and integration with downstream Generative AI applications.
• Contribute to thought leadership at the intersection of Data Engineering, Knowledge Graphs, and Generative AI.
• Present, discuss, and explain how high‑quality Knowledge Graph data can benefit business use cases, and proactively propose new cases to relevant stakeholders.

What you bring

• PhD or Master’s degree in computer science, artificial intelligence, physics, mathematics or other relevant disciplines.
• 2+ years of related professional experience with a strong background in Data Engineering, ideally including working with Knowledge Graphs in a business context.
• Experience designing and operating large‑scale data pipelines (batch and streaming) using modern data engineering tooling.
• Knowledge of SAP data and SAP applications – their data models, business objects, and extraction interfaces.
• Experience mapping business operations and source systems into clear data models, ontologies, and organized schemas.
• Awareness of latest trends at the intersection of Data Engineering, Knowledge Graphs, and LLMs – e.g. KG construction from unstructured data, graph RAG, KG embeddings.
• Solid command of SQL, modern data warehousing/lakehouse concepts, and cloud data platforms (e.g., AWS, GCP, Azure, Databricks).
• Experience in Data Engineering with a hands‑on approach to solving data challenges.
• Fluent in Python and the ability to convert your ideas into productive code.
• Working proficiency in the RDF Knowledge Graph technology stack (RDF, RDFS, OWL, SHACL, SPARQL) and hands‑on experience operating triple stores – familiarity with property graph solutions (e.g., Neo4j) is a plus.
• Working knowledge of the current triple store ecosystem (commercial and open‑source, e.g., GraphDB, Stardog, Virtuoso, Apache Jena, Blazegraph) and Enterprise Knowledge Graph architectures is a plus.
• Ability to advise diverse stakeholders on how to bring their data into an enterprise Knowledge Graph and apply it to business use cases.
• Strong communication and collaboration skills, with the ability to work effectively in cross‑cultural teams.

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