Senior Data Engineer

A pioneering biotech company dedicated to advancing longevity and regenerative medicine. By harnessing breakthroughs in cellular biology, AI-driven analytics, and bioengineering, we develop cutting-edge solutions to enhance health and extend lifespan. Our multidisciplinary team of scientists, data engineers, and researchers collaborates to drive innovation in ageing research, biomarker discovery, and precision health technologies.

As part of our dynamic team, you will contribute to groundbreaking research in longevity and regenerative medicine. Working closely with data engineers and scientists, you will apply advanced analytics, AI-driven insights, and bioengineering techniques to develop innovative solutions that improve health and extend lifespan.

Team of Data Scientists and Engineers.

• Interview process that respects people and their time
• Professional and open IT community
• Internal meet-ups and resources for knowledge sharing
• Time for recovery and relaxation
• Bright online and offline events
• Opportunity to become part of our internal volunteer community

• Design, build, and maintain ETL/ELT pipelines to process and transform data efficiently.
• Develop and optimize scalable data architectures in the cloud, preferably GCP.
• Implement and maintain data catalog solutions to ensure discoverability and governance.
• Build APIs and integrations for seamless data exchange across systems.
• Perform data quality checks and implement automated testing frameworks to ensure data accuracy and reliability.
• Collaborate with teams to build self-service systems and promote data democratization.
• Document and maintain data engineering processes and best practices.

• To be successful in this role, it will be helpful to have at least 3+ years of experience as a Data Engineer, have a genuine interest in the biotech domain and have some of the following below:
• Strong experience in data engineering and cloud platforms (preferably GCP).
• Proficiency in programming languages like Python, SQL, and shell scripting.
• Familiarity with data catalog tools (e.g., DataHub, Apache Atlas) and metadata management.
• Experience with building and maintaining scalable ETL pipelines.
• Understanding of API development and integration.
• Knowledge of data governance and data quality principles.
• Background in biological or scientific data is a plus but not mandatory.
• Domain expertise can be substituted for formal education.
• Strong problem-solving skills and ability to work with cross-functional teams.
• Excellent communication skills in English, both written and verbal.