Senior R&D Data Engineer
Responsibilities
Data Engineering & Pipeline Development- Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners).
- Create and optimise data flows for structured and unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake, and other modern engineering tools.
- Develop and maintain specific data repositories, implementing enterprise‑level data models, and creating new models as needed.
- Enable AI/ML readiness by ensuring data is well‑structured, versioned, traceable, and semantically aligned with enterprise data standards.
- Partner with data scientists, domain experts, and digital technology teams to translate business needs into high‑quality data products and engineering requirements.
- Work closely with ontology/knowledge graph teams to implement semantic models and future‑proof data architectures.
- Implement data quality and performance standards; define KPIs to measure accuracy, completeness, and consistency across the data assets.
- Apply data versioning and lineage tracking for compliance, traceability, and audit readiness.
- Follow software development best practices including code versioning, DevOps integration, and documentation.
- Engage with scientific, technical, and operations stakeholders to understand requirements, design data solutions, and drive adoption.
- Support multiple concurrent projects, managing priorities, and delivering maximum business value across the network.
Requirements
- Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred.
- 3+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment.
- Proficiency with Python, R, SQL, and cloud-based architectures (AWS services, Snowflake, Databricks, Redshift).
- Expertise in ETL and DWH.
- Experience with NoSQL and graph databases.
- English language proficiency of B2+
- Strong analytical, problem‑solving, and stakeholder‑management skills, with the ability to translate discussions into actionable requirements.
- Ability to drive multiple exciting projects simultaneously with strong organizational skills and adaptability.
Nice to have
- Experience with regulated or standards‑driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards.
- Familiarity with high‑dimensional data (e.g., imaging, sensor data, etc).
- Experience with principles connecting to or feeding MLOps and model deployment workflows.
- Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems.
- Exposure to knowledge graph or ontology‑driven architectures.
We offer
- Competitive compensation
- Remote or office work
- Flexible working hours
- Healthcare benefits: medical insurance and paid sick leave
- Continuous education, mentoring, and professional development programs
- A team with an excellent tech expertise
- Certifications paid by the company
