Data Engineer

Role: Data Engineer with Life Science Location: San Francisco, CA ( 4 days from Office) Contract role Looking for 10+ years experience What’s in it for you :- Opportunity to lead technical teams, guide architecture decisions, and influence migration strategies to AWS. Job Description: Key Responsibilities ● Data Migration and Integration: ○ Migrate assay and compound data from legacy systems into other architectures, ensuring data integrity and security. ○ Implement robust ETL pipelines for processing and integrating data efficiently from multiple sources. ● Database Development: ○ Design, develop, and maintain scalable and high-performing relational databases (e.g., Oracle, PostgreSQL) to host and manage experimental and prediction data. ○ Optimize database structures for querying, storage, and scalability. ● Visualization Tool Integration: ○ Collaborate with teams to integrate data into existing visualization tools and frameworks. ○ Develop and enhance plugins for visualization tools to deliver interactive and meaningful insights for scientific teams. ● Collaboration and Communication: ○ Work closely with pRED teams in Europe, ensuring alignment in goals and timelines. ○ Collaborate with scientists, data engineers, and software developers to define requirements and deliver user-centric solutions. Must-Have Qualifications ● Database Expertise: Proven experience with relational databases like Oracle and PostgreSQL, including design, optimization, and migration. ● Programming Skills: Strong proficiency in Python, especially for backend development and data handling. Java is an added advantage. ● ETL Development: Expertise in building Extract, Transform, and Load (ETL) processes to effectively process and migrate assay/compound data. ● Integration Experience: Familiarity with integrating data into visualization platforms and crafting modular plugins or interfaces. ● Team Collaboration: A demonstrated ability to work with globally distributed teams across time zones to deliver complex projects. Nice-to-Have Skills ● Data Engineering Knowledge: Skills in building data pipelines, data modeling, and working with modern cloud platforms such as AWS or GCP. ● Visualization Expertise: Experience with visualization tools such as Vortex or D360. ● Domain Knowledge: Familiarity with laboratory workflows, assay data, compound data, or related pharmaceutical/life sciences data. ● Plugin Development Tools: Experience with APIs or SDKs for creating custom visualization tool integrations. ● DevOps Practices: Familiarity with Docker, Kubernetes, or CI/CD pipelines to streamline development and deployment. Key Attributes ● Strong analytical and problem-solving skills with attention to detail. ● Excellent communication skills to bridge gaps between scientific and technical teams. ● Highly adaptive and able to manage competing priorities in a dynamic, fast-paced environment. ● Self-motivated and able to independently drive progress while collaborating with globally distributed teams.