Graph Engineer

Migx; Barcelona; Híbrido; EUR 65.000 - 95.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

25 holiday days per year

Social benefits package

Career development opportunities

Employee-centric culture

Training programs

International exposure

Friendly atmosphere

Descripción de la vacante

Migx in Barcelona is seeking a hands-on Graph Engineer to design, build, and operate Stardog-based knowledge graphs for life science clients. You will work with the Knowledge Management team to translate data models into scalable graph infrastructure, integrating diverse data sources via modern pipelines and applying ontology concepts to production systems.

You will implement virtual graphs, SMS mappings, and SPARQL tooling with a pragmatic engineering mindset, contributing to documentation and

Formación

• Hands-on experience with Stardog or a comparable enterprise RDF triplestore.
• Solid experience with Stardog virtual graphs and linking external data sources.
• Experience creating and maintaining SMS mappings.
• Data engineering experience: building and operating data pipelines, ETL/ELT, data integration.
• Strong understanding of RDF/OWL ontology concepts.
• Proficient in SPARQL and graph query optimization.
• Independent and reliable with ownership of technical topics.
• Pragmatic, production-focused mindset; documentation-ready.
• Proficient in using AI tools to improve productivity.
• Languages: English fluency; Spanish a plus.

Responsabilidades

• Design, build, deploy, and operate Stardog knowledge graphs.
• Create and maintain Stardog virtual graphs linking data sources.
• Write SMS mappings to translate schemas into RDF; ensure performance.
• Write and optimize SPARQL queries and Stardog tooling for loading and validation.
• Configure Stardog security, performance, and production readiness.
• Monitor and tune graph database performance across layers.
• Design and implement data pipelines ingesting to RDF graphs.
• Develop ETL/ELT processes with data quality checks and tests.
• Integrate knowledge graphs with upstream/downstream systems and analytics.
• Apply software engineering practices: version control, CI/CD, documentation, code review.
• Support ontology versioning, SHACL validation, and alignment.
• Document data models, mappings, and graph architecture for reuse.

Conocimientos

Stardog

Virtual graphs

SMS mappings

Data engineering

RDF/OWL

SPARQL

Documentation

Independent work

AI tools

English (Fluent)

Spanish (optional)

Herramientas

Databricks

Azure Stack

ADLSv2

ADF

AKS

Descripción del empleo

Position Name

Graph Engineer

About the profile

We are seeking a hands-on Graph Engineer (primarily using Stardog) to design, build, and operate knowledge graph solutions that turn complex, siloed data into a connected, queryable asset for our life science clients. In this role, you will work closely with the Knowledge Management team and client stakeholders to translate data models and business questions into robust, production-grade graph infrastructure.
Acting as a technical specialist internally and externally, you will build and maintain Stardog-based knowledge graphs, integrate diverse data sources through modern data engineering pipelines, and ensure the ontologies and data models underpinning our graphs are sound, scalable, and maintainable.
You bring strong hands-on experience with Stardog and data engineering, a solid understanding of ontology management concepts (RDF/OWL), and a pragmatic engineering mindset. Experience with Databricks, or in life science IT and regulated environments, is a plus and will be considered a strong asset.

Responsibilities

• 1. Knowledge Graph Engineering with Stardog
  • Design, build, deploy, and operate knowledge graphs on Stardog, including data modeling, virtual graphs, and reasoning configuration
  • Create and maintain Stardog virtual graphs, connecting relational and external data sources to the knowledge graph without physical replication
  • Design, write, and maintain SMS (Stardog Mapping Syntax) mappings to translate source schemas into RDF, ensuring accuracy, performance, and maintainability
  • Write and optimize SPARQL queries and Stardog-specific tooling (Stardog Studio, CLI, APIs) for data loading, validation, and troubleshooting
  • Configure and maintain Stardog security, performance, and scalability settings for production workloads
  • Monitor, tune, and troubleshoot graph database performance across ingestion, reasoning, and query layers
• 2. Data Engineering & Integration
  • Design and implement data pipelines to ingest, transform, and map source data (relational, document, file-based) into RDF graph structures
  • Build and maintain ETL/ELT processes, including source-to-target mapping definitions, data quality checks, and automated testing
  • Integrate the knowledge graph with upstream and downstream systems, APIs, and analytics layers
  • Apply sound software engineering practices: version control, CI/CD-aware delivery, documentation, and code review
• 3. Ontology & Data Modeling Support
  • Apply ontology management concepts (RDF, RDFS, OWL) to implement and maintain data models designed in collaboration with ontologists and business stakeholders
  • Support ontology versioning, validation (e.g. SHACL), and alignment across data sources
  • Contribute to documentation of data models, mappings, and graph architecture so others can understand, reuse, and build on the work

Requirements - Must have

• Hands-on experience with Stardog (or a comparable enterprise RDF triplestore/graph database)
• Solid understanding of Stardog virtual graphs and experience connecting external/relational data sources to a knowledge graph without full data replication
• Practical experience creating and maintaining SMS (Stardog Mapping Syntax) mappings
• Solid experience in data engineering: building and operating data pipelines, ETL/ELT, and data integration
• Good understanding of ontology management concepts, including RDF and OWL
• Comfortable working with SPARQL and graph query optimization
• Independent and reliable, able to take ownership of technical topics and drive them forward
• Pragmatic, focused on what works in real-world production environments rather than theoretical perfection
• Well-organized, documenting your work clearly and digitally so others can understand and reuse it
• Comfortable using AI tools, applying them thoughtfully to improve productivity without over-reliance

Requirements - Nice to have

• Experience with Azure Stack: ADLSv2, ADF, AKS, etc.
• Experience with Databricks (Spark-based data engineering, Delta Lake, notebooks)
• Experience in Life Sciences IT, particularly in regulated environments (e.g. GxP, validated systems, data traceability requirements)

Seniority Level

Mid

Languages

Fluent English written and spoken. Other languages a plus (especially Spanish)

What we offer

• Hybrid work model and flexible working schedule that would suit night owls and early birds.
• 25 holiday days per year.
• Attractive social benefits package.
• Opportunities for career development and the opportunity to shape the company's future.
• An employee-centric culture directly inspired by employee feedback - your voice is heard, and your perspective encouraged.
• Different training programs to support your personal and professional development.
• Work in a fast growing, international company.
• Friendly atmosphere and supportive Management team.

This is an opportunity to be at the forefront of the semantic data revolution in life …