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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Data Engineer - **Company:** Zifo RnD Solutions - **Location:** Belgium - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Component-Based Software Engineering, Application Layers, Audit Trail, Automation of Tests, Big Data, Bioinformatics, Code Review, Continuous Integration, Customer Data Management, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Data Vault Modeling, Database Testing, Graph Database, Identity and Access Management, Metadata, Service Design, Software Engineering, TypeScript, ReactJS, Large Language Models, Snowflake, Fastapi, Data Layers, AI Platforms, Information Technology, Graphql, Machine Learning Operations, Data Pipelines, Databricks - **Published:** September 1, 2026 - **Apply:** https://be.indeed.com/viewjob?jk=293edacb2c91b8ae ## About the Role Bachelor's or Master's (or equivalent practical experience) in Computer Science, Data Engineering, Life Sciences, Bioinformatics or a related field. Multiple years in data engineering, analytics engineering, software engineering or AI platform roles, with at least 3 years operating at technical lead/senior level guiding design and delivery across multiple teams or workstreams. Experience operating in a matrixed environment. Required skills Deep, production-grade experience on a modern cloud data platform - Databricks and/or Snowflake. A track record of shipping data products in pharma or biotech. Desirable skills Data modelling for analytical and product use - dimensional, data vault, medallion/lakehouse patterns. Engineering practice: CI/CD, automated testing (including data testing), infrastructure-as-code, observability, access management, cost awareness. API and service design; comfort with the application layer that sits on top of the data (e.g. FastAPI, REST/GraphQL, containerised services). Full-stack familiarity (React/TypeScript) sufficient to lead a squad that owns the whole product. GenAI/LLM engineering in a regulated setting: RAG over scientific documents, evaluation frameworks, guardrails, LLMOps, and a realistic view of where GenAI does and does not belong. MLOps and model lifecycle management (MLflow or equivalent). Metadata, catalog and data quality tooling; semantic layers; knowledge graphs for scientific data. FAIR data principles applied in practice. Experience with scientific data management platforms. ## Description We are looking for a hands-on Principal Data Engineer to act as the primary technical reference for building R&D data products. The role sits at the intersection of scientific domain, data platform, and engineering delivery. You will guide technical design and implementation across data engineering, analytics, AI and application components within an R&D data and AI ecosystem, while carrying accountability for engineering quality, mentoring and delivery outcomes. This is not a coordination role. You will write code, review code, and own challenging technical problems in diverse innovation ecosystems. Role Responsibilities Technical leadership & hands-on delivery Provide hands-on technical expertise across R&D data, analytics, AI and application components, guiding design and implementation from prototype through to validated production. Act as the technical escalation point for complex engineering challenges spanning data pipelines, analytics, AI services and full-stack applications. Own end-to-end technical design for data products: ingestion, modelling, curation, serving, and the consuming application or API layer. Make and document architecture decisions, including technology evaluation and integration into an existing large-scale data ecosystem. Ensure delivery commitments are met without trading away reliability, security, maintainability or data integrity. Collaboration & mentoring Align with enterprise and software architects so that squad-level implementation stays homogenous with the wider ecosystem. Mentor data engineers, full-stack engineers and data scientists. Lead code reviews and set engineering standards (branching strategy, testing expectations, definition of done, observability baseline). Identify skill gaps across the team and contribute to capability building, reusable assets and knowledge sharing. Translate between scientific stakeholders and engineers. Governance, security & compliance Ensure solutions meet data governance, security and compliance requirements relevant to regulated R&D data. Embed governance-by-design: lineage, cataloguing, access control, data quality rules and audit trail designed in from the start.