Solutions Engineer
Siemens Plc
United Kingdom
about 1 month ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
Tech stack
Artificial Intelligence
Data Cleansing
Information Engineering
Query Languages
Graph Database
Neo4j
Mendix
SPARQL
Enterprise Software Applications
Ontology Design
Build Management
Operational Systems
Job description
The Technical Solutions Marketing team builds technical credibility for Mendix through validated architectures, reusable proof points, and real-world implementations. In this role, you will work directly with customers and the field to prove high-value use cases through lighthouse implementations, then package what works into reusable patterns, components, and technical narratives that scale across GTM and delivery ecosystems. Responsibilities
- Design and build knowledge graphs and ontologies that underpin Mendix’s AI solutions, technical demos, and customer implementations.
- Own the data and graph architecture workstream within integrated team solutions, working alongside Solutions Engineers who cover the application and AI layers.
- Turn validated customer work into reusable assets: reference architectures, blueprints, accelerators, and repeatable patterns.
- Contribute to customer engagements, events, and analyst briefings as the team’s expert in data and graph.
- Ground GTM positioning and storytelling in validated technical proof points, supporting high-impact moments like executive demos and analyst briefings., * Knowledge graph demos and reference architectures that hold up in customer and analyst sessions and are ready to reuse across the team.
- Recognised as the team’s go-to on data and graph in customer engagements.
- Packaged architectures adopted by the team and reused across go-to-market and delivery.
- Implementation learnings feeding product direction and positioning in a consistent way.
Requirements
- Demonstrable hands-on experience designing and building knowledge graphs and ontologies.
- Strong understanding of graph data modelling, semantic modelling, and ontology design.
- Experience with graph database platforms and query languages (e.g. SPARQL, Cypher, or equivalent).
- Data engineering fundamentals: pipelines, data preparation, and integration with enterprise systems (ERP, CRM, operational systems).
- Ability to translate data and graph architectures into reusable patterns and clear technical narratives.
- Strong communication and collaboration skills across Product, GTM, engineering, and customer stakeholders.
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