Solutions Engineer

SIEMENS; Greater London; On-site; GBP 85,000 - 120,000; Full time

Job summary

Siemens Industry Software GmbH, part of Mendix, seeks an experienced Solutions Engineer to design and build knowledge graphs and ontologies supporting AI-enabled Mendix solutions. You will own data/graph architecture within integrated solutions and translate architectures into reusable patterns and technical narratives.

Collaborating with Solutions Engineers and GTM teams, you will translate customer work into reference architectures, blueprints, accelerators, and playable demos, ensuring

Qualifications

• Hands-on experience designing and building knowledge graphs and ontologies.

Responsibilities

• Design and build knowledge graphs and ontologies that underpin Mendix's AI solutions and customer implementations.

Skills

Knowledge graphs

Ontology design

Data modelling

Communication across stakeholders

Tools

Graph databases

SPARQL

Cypher

Job description

Job ID 508083

Posted since 27-May-2026

Organization Digital Industries

Field of work Product Management, Portfolio & Innovation

Company Siemens Industry Software GmbH

Experience level Experienced Professional

Job type Full-time

Work mode Office/Site only

Employment type Permanent

Location(s)

• London - London City of - United Kingdom
• Rotterdam - Zuid-Holland - Netherlands

Solutions Engineer (Data & Graph)

About the Role

Mendix, a Siemens business, powers hybrid agentic workforces by enabling organizations to build, scale and orchestrate agents and applications faster by combining low-code, AI, data, and integrations into a unified platform.

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.

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.

Note: This is not a data analyst or business intelligence role. The focus is graph data, knowledge modelling, and how that layer supports AI and agentic applications.

What Success Looks Like

• 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.

Why this role

This role sits at the foundation of agentic enterprise solutions are grounded in real data, and plays a central part in how knowledge graph architectures are proven, packaged, and scaled.

You will design the graph layer that gives AI agents structured context and memory at enterprise scale, work alongside the application and AI Solutions Engineers to take those implementations in front of customers and analysts, and feed what you learn back into Product and Go To Market teams.