Data Engineer - Remote - Spain

A World-Changing Company at Keystone AI At Keystone AI , we're driven by the mission to make complex data analysis simpler and more accessible. We empower key institutions worldwide to revolutionize their data utilization and technology integration. Keystone AI is powering the world’s federated data platforms for population-scale health and life sciences. Our Federated AI Platform is trusted across national health systems, global pharma, and research biobanks to unlock sensitive biomedical and real-world data securely, compliantly, and at scale. As we scale through our Series B growth phase, we are expanding our product organization to meet the demands of a global market. Join us if you're inspired to tackle meaningful challenges and want to lead the next generation of health technology and cloud-based genomic solutions. The Role As a Data Engineer in our Spain hub, you will bridge the gap between complex biological science and robust software engineering. You will be responsible for building the scalable infrastructure that powers our scientific discoveries, ensuring that our data pipelines are not only high-performing but also strictly compliant with European data protection standards. This is a senior individual contributor (IC) role in our engineering framework — ideal for someone who thrives in complex problem-solving, leads implementations for major features, and mentors others in technical excellence. You’ll work with distributed systems, APIs, and data services that underpin Keystone AI ’s products — ensuring performance, security, and reliability at scale. Your contributions will directly advance scientific discovery and precision health globally. Your Role and Responsibilities Technical Leadership Architectural Ownership: Lead the implementation of challenging features across distributed architectures, ensuring systems are performant, maintainable, and secure. System Integrity: Design software with built-in resilience and observability; balance immediate product delivery with long-term platform sustainability. Standards Advocate: Champion engineering best practices, including test automation, CI/CD, and high code quality standards. System Design: Proactively identify technical bottlenecks and contribute to cross-team architecture reviews. Delivery Excellence Technical Translation: Convert complex scientific requirements into actionable engineering tasks for the development team. Cross-Functional Sync: Collaborate with Product, Science, and DevOps to define milestones and ensure projects move from conception to production with measurable impact. Incident Management: Lead root cause analysis (RCA) for live incidents and implement permanent structural solutions. Collaboration & Communication Stakeholder Management: Communicate technical concepts effectively to a diverse audience, ranging from cloud engineers to molecular biologists. Mentorship: Provide guidance and peer feedback to junior and mid-level engineers to foster a culture of continuous learning. Data & Domain Awareness Scientific Alignment: Apply your background in bioinformatics or computational biology to ensure solutions align with genomics or clinical data use cases. Security & Compliance: Build systems that protect sensitive biomedical data, adhering strictly to GDPR (RGPD in Spain), HIPAA, and ISO27001 standards. What You Bring to the Table Knowledge Graphs & Semantic Layers: Expert in designing biomedical knowledge graphs and semantic data models that make implicit relationships in biomedical data explicit and machine-interpretable for LLMs and AI agents to reason over at scale. AI/LLM Infrastructure & Context Engineering: Hands-on experience integrating large language models into data systems, building RAG (Retrieval-Augmented Generation) frameworks, and architecting systems where rich metadata, lineage, and business semantics are embedded to enable reliable AI reasoning. Modern Data Fabric Architecture: Proven expertise designing data fabrics and federated data models that connect distributed biomedical data sources (genomics, clinical, imaging, drug discovery) while maintaining provenance, governance, and optimizing for AI/ML workload consumption—not traditional human dashboards. Education: MSc in Computer Science, Bioinformatics, Computational Biology, or a closely related advanced technical field. Experience: 5+ years in software engineering or computational systems development, preferably in data-heavy or regulated sectors. Tech Stack: Strong programming skills (Python, Go, Java, or similar) and familiarity with microservice or distributed system architectures. Infrastructure: Proven experience designing and maintaining production-grade systems (preferably in data-heavy or regulated environments). Hands-on experience with cloud platforms (AWS, Azure, or GCP), CI/CD pipelines, and container orchestration (Docker, Kubernetes). Data & Security: Working understanding of data privacy, observability, and security principles. Problem Solving: A pragm