Senior Data Engineer

Zifo; España; On-site; EUR 75,000 - 110,000; Full time

Job summary

Zifo is seeking a hands-on Senior Data Engineer to be the primary technical reference for building R&D data products. You will lead end-to-end design and implementation across data engineering, analytics, AI, and application layers within a regulated R&D data ecosystem.

You will write and review code, mentor engineers, and uphold high standards of quality and delivery. In this role you will own data product design from ingestion to serving, ensure governance and security, and align with

Qualifications

• Bachelor's or Master's in Computer Science, Data Engineering, Life Sciences, Bioinformatics or a related field.
• 3–5+ years in data engineering, analytics engineering, software engineering or AI platform roles.
• Experience operating in a matrixed environment.

Responsibilities

• Provide hands-on technical expertise across R&D data, analytics, AI and application components, guiding design and implementation from prototype through to 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 compromising reliability, security, maintainability or data integrity.

Skills

Databricks

Snowflake

Data modelling

CI/CD

Automated testing

MLOps

GenAI / LLM engineering

APIs

Full-stack familiarity

Knowledge graphs

Education

Bachelor's or Master's in Computer Science / Data Engineering / Life Sciences

Tools

FastAPI

REST/GraphQL

Docker

Kubernetes

MLflow

Data governance tools

Job description

We are looking for a hands-on Senior 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.
• Align with enterprise and software architects so that squad-level implementation stays homogenous with the wider ecosystem.
• Lead code reviews and set engineering standards (branching strategy, testing expectations, definition of done, observability baseline).
• 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.
• Partner with data governance, platform and architecture teams on standards adoption.

Qualifications & experience

• Bachelor's or Master's (or equivalent practical experience) in Computer Science, Data Engineering, Life Sciences, Bioinformatics or a related field.
• 3-5+ years in data engineering, analytics engineering, software engineering or AI platform roles.
• 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.
• 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).
• 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.

About Zifo:

Zifo is a global R&D solutions provider focused on the industries of Pharma, Biotech, Manufacturing QC, Medical Devices, specialty chemicals and other research-based organizations. Our team’s knowledge of science and expertise in technology help Zifo better serve our customers around the globe, including 7 of the Top 10 Biopharma companies.

A passion to learn and a spirit of teamwork characterizes us. We revere excellence and the value everyone brings. At Zifo, our culture is one where we debate, challenge ourselves and interact with all alike.

We look for Science - Biotechnology, Pharmaceutical Technology, Biomedical Engineering, Microbiology etc. We possess scientific and technical knowledge and bear professional and personal goals. While we have a "no doors" policy to promote free access within, we do have a tough door to walk in. We search with a two-point agenda - technical competency and cultural adaptability.