Data Engineer

Accountabilities

In this role, you will be responsible for designing, developing, and maintaining scalable data systems that support large and diverse datasets.

• Design, build, and maintain scalable data pipelines across multiple environments and data sources.


• Integrate structured and unstructured data into data warehouses and data lakes.


• Collaborate with cross-functional teams to ensure data quality, consistency, and accessibility.


• Develop and optimize ETL/ELT workflows for performance, reliability, and maintainability.


• Build and refine database schemas, data models, and technical documentation.


• Implement data governance, security standards, and compliance best practices.


• Monitor data systems and continuously improve integrity, scalability, and efficiency.

Requirements
We are looking for a strong backend-oriented Data Engineer with solid experience in Python and modern data engineering practices.

• Strong experience in backend development using Python, with clean and scalable architecture practices.


• Proficiency with frameworks such as FastAPI, Django REST Framework, or Flask.


• Solid understanding of SQL, database modeling, and query optimization.


• Hands-on experience building and maintaining ETL/ELT pipelines.


• Experience integrating data from multiple sources such as APIs, databases, and streaming systems.


• Familiarity with distributed systems or large-scale data processing tools.


• Understanding of data governance, security, and data quality principles.


• Exposure to cloud platforms such as AWS, Azure, or GCP is a plus.


• Knowledge of Docker, Kubernetes, and CI/CD tools is advantageous.


• Experience in life sciences, biotech, genomics, or clinical data environments is a strong plus.


• Strong collaboration skills and ability to work effectively in distributed, international teams.

Benefits

• Competitive B2B compensation up to 23,000 PLN net/month.


• Fully remote work with global collaboration opportunities.


• Modern work equipment (MacBook or ThinkPad with Linux environment).


• Budget for professional development, including training, certifications, and conferences.


• Opportunity to work on high-impact projects in biotech, pharma, and enterprise analytics.


• Exposure to cutting-edge data engineering and scientific data challenges.


• Collaborative, supportive, and high-performing engineering culture.

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