platform engineer for scientific data

Quantori is an international team providing custom software solutions that enable advanced analytics and digital process transformation.

• Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems, including lab systems, MES, clinical supply, quality systems, and external partners;
• Create and optimize structured and unstructured data flows using Python (PySpark), R, SQL, Databricks, and other modern engineering tools;
• Develop and maintain specific data repositories, implement enterprise-level data models, and create new models as needed;
• Enable AI/ML readiness by ensuring data is well-structured, versioned, traceable, and semantically aligned with enterprise data standards;
• Communicate closely with stakeholders and other teams.

• Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or a related field;
• 6+ Years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment;
• Strong expertise with the Databricks platform;
• Proficiency with Python, SQL, cloud-based architectures, AWS services, Spark, and dbt;
• 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 projects simultaneously with strong organizational skills and adaptability;
• Nice to have: advanced degree, CI/CD and infrastructure tools such as Terraform, regulated or standards-driven data environments including CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards, high-dimensional data such as imaging and sensor data, principles connecting to or feeding MLOps and model deployment workflows, manufacturing systems (MES), laboratory information systems, industrial data systems, knowledge graph or ontology-driven architectures.

• Competitive compensation;
• Flexible working hours;
• Continuous education, mentoring, and professional development programs;
• A team with excellent tech expertise.