Senior Data Engineer

We are seeking a Senior Data Engineer to design, build, and optimize data capture, processing, and storage solutions that enable advanced analytics and digital process transformation. This role involves creating robust, future-proof data systems, engineering workflows, and high-value data repositories that support scientific, technical, and operational decision-making.

Responsibilities

• 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 optimize 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.

Requirements

• Bachelor's degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred.

• 6+ 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) along with Spark and dbt.

• Familiarity with Databricks AI/BI or Tableau or other BI tools.

• 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 work

• Flexible working hours

• Continuous education, mentoring, and professional development programs

• A team with an excellent tech expertise