Senior Knowledge Graph Engineer

for Biotech Company

Location

Sao Paolo, Brazil, Remote

Area

AI/ML/CV/NLP

Tech Level

Senior

Tech Stack

LLM, Agentic AI, Python

About the Client

A pioneering biotech company dedicated to advancing longevity and regenerative medicine. By harnessing breakthroughs in cellular biology, AI-driven analytics, and bioengineering, we develop cutting-edge solutions to enhance health and extend lifespan. Our multidisciplinary team of scientists, data engineers, and researchers collaborates to drive innovation in ageing research, biomarker discovery, and precision health technologies.

Project details

We are finding new ways to treat disease by targeting the mechanisms of aging, with the ultimate goal of increasing healthy human lifespan.

We discover drug targets for aging and age-related disease by combining longitudinal human data with causal evidence from genetics and animal models, structured ontologies, and curated external knowledge. A knowledge graph harmonizes these disparate sources to enable scientific discovery of targets, indications, and mechanisms of action.

We are seeking a Knowledge Graph Engineer with hands-on experience in data engineering and graph technologies to design, build, and maintain scalable knowledge graph solutions. You will translate ontologies and semantic models into production systems, develop pipelines that connect enterprise data to graph platforms, and build queries that support downstream analytics and intelligent applications.

This role is ideal for someone who combines strong software engineering fundamentals with an understanding of graph modelling and semantic data. You will own solutions from architecture and technology selection through implementation, testing, and performance optimization, working closely with data engineers, AI teams, and stakeholders to turn business requirements into reliable systems.

Your Team

We’re looking for an experienced Engineer to join our small, but powerful cross-functional team of engineers and biologists. You’ll contribute to projects that deliver value in fast, iterative cycles, while learning how to adapt in a dynamic startup environment where priorities can shift quickly.

What's in it for you

• Interview process that respects people and their time
• Professional and open IT community
• Internal meet-ups and resources for knowledge sharing
• Time for recovery and relaxation
• Bright online and offline events
• Opportunity to become part of our internal volunteer community

Responsibilities

Maintain and extend our knowledge graph, including its schema, ingestion pipelines and query layer (Neo4j, Cypher, Apache Jena Fuseki, BigQuery on Google Cloud).

Build and run validation and data-governance pipelines to maintain data quality, consistency, and performance. This covers schema and constraint checks, identifier reconciliation, provenance and versioning.

Maintain our ontologies and semantic models with the biology and data science teams, and develop the mapping tool scientists use to annotate new terms and analyses. This includes the validation that keeps their mappings consistent with the graph.

Integrate knowledge graph solutions with cloud platforms and enterprise data ecosystems, including Google Cloud and BigQuery.

Collaborate with data engineers, data science, AI and biology teams to make graph data accessible and useful for downstream applications.

Write clean, testable, production-grade code and apply engineering best practices throughout development and delivery.

Skills

Basic qualifications

• Bachelor’s degree or higher in computer science, data engineering, AI or a related field, with 3+ years of industry experience in data engineering.
• Experience working with biological or biomedical data, such as genomics, proteomics, clinical or phenotype data, and familiarity with standard biomedical ontologies and identifiers (e.g. UniProt, Gene Ontology, EFO, MONDO).

Technical qualifications

• Hands-on experience with graph databases such as Neo4j, Stardog or GraphDB, including writing and optimizing queries in Cypher or SPARQL.
• Working knowledge of semantic modelling concepts and standards such as RDF and OWL, and the ability to turn an ontology into a working schema.
• Experience designing and building APIs (e.g. REST or GraphQL, using frameworks such as FastAPI) that give applications and users access to graph data.
• Experience building data-quality or validation frameworks.
• Familiarity with Google Cloud and BigQuery.
• Strong Python and working knowledge of JavaScript for building data pipelines, graph integrations, and production applications.
• Strong software engineering practices, including data modeling, system design, testing, documentation and version control.
• (Preferred) Experience with LLM or agent systems
• (Preferred) Experience with Open Targets and other drug-discovery knowledge bases.

Collaboration and ownership

• Strong problem-solving skills and sound technical judgment when requirements are still evolving or design priorities compete.
• Ability to take ownership of a solution from initial design through implementation and ongoing maintenance.
• Clear written and verbal communication, including the ability to explain architecture decisions and technical trade-offs to clients and stakeholders.
• Ability to work effectively across data engineering, biologists and AI teams.

Your personal recruiter

Valentina Brysina