Staff Data Engineer
- Architect and Implement Scalable Data Pipelines: Design, build, and maintain high-performance, fault-tolerant data pipelines for processing large-scale scientific and clinical datasets.
- Drive Data Engineering Best Practices: Establish and champion best practices for data modeling, ETL/ELT processes, data quality, and data governance across late-stage discovery and clinical operations.
- Enhance Workflow Orchestration: Leverage and extend our existing orchestration platforms (e.g., Camunda, Prefect) to integrate data pipelines seamlessly into end-to-end scientific and clinical workflows, ensuring automation and reliability.
- Ensure GxP/GDPR/HIPAA Compliance: Design and implement data systems and processes that adhere to clinical regulations, 21 CFR Part 11, and other relevant industry standards, ensuring data integrity, auditability, and validation readiness.
- Collaborate with Cross-Functional Teams: Partner closely with data scientists, clinical operations specialists, and other engineering teams to understand data needs, translate requirements into technical solutions, and ensure successful data delivery.
- Optimize Data Performance and Cost: Continuously monitor, analyze, and optimize the performance and cost-efficiency of data processing and storage solutions.
- Provide Technical Leadership and Mentorship: Act as a technical leader within the team, providing guidance, mentorship, and code reviews to junior and mid-level engineers, fostering a culture of excellence and continuous learning.
- Troubleshoot Complex Data Issues: Lead the diagnosis and resolution of complex data-related issues, ensuring minimal disruption to scientific and clinical operations.
- Contribute to Strategic Planning: Participate in the strategic planning and roadmap development for data engineering initiatives within the late-stage discovery and clinical domains.
- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience.
- Minimum of 7+ years of experience in data engineering, with a strong focus on building and managing large-scale data pipelines.
- Demonstrated experience with GCS, distributed data processing, and machine learning workflows
- Strong programming skills in Python. R is also a plus.
- In-depth understanding of data warehousing, data lakes, and modern data architecture patterns.
- Proven experience designing and implementing systems compliant with GxP regulations (e.g., GCP, GLP, GMP), 21 CFR Part 11, and other relevant regulatory frameworks.
- Experience with workflow orchestration tools (e.g., Prefect, Camunda, Airflow)
- Familiarity with cloud platforms, particularly Google Cloud Platform (GCP) and Kubernetes
- Excellent problem-solving, analytical, and debugging skills.
- Strong communication, collaboration, and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Ability to work effectively in a fast-paced, dynamic environment and manage multiple priorities.
- We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust.
- We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.
- We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.
- We move with urgency because patients are waiting. Speed isn't about rushing but about moving the needle every day.
- We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy-leaders take accountability for decisive action, and teams own outcomes together.
- We are One Recursion. True cross-functional collaboration is about trust, clarity, humility, and impact. Through sharing, we can be greater than the sum of our individual capabilities.
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