Data Engineer, Python Developer

Role Overview

• Collaborate with scientists, assay teams, and lab operations to capture end-to-end assay and experimental workflows, from sample onboarding and execution through data ingestion, validation, and downstream analytics
• Translate scientific and operational requirements into well-defined functional, technical, and data requirements for laboratory platforms, system integrations, and next-generation data pipelines
• Design, develop, and maintain Python-based backend services, APIs, and data pipelines on AWS
• Build backend services using FastAPI and supporting frameworks such as Flask or Django
• Develop and maintain RESTful APIs and microservices for integration with scientific systems including Benchling, LIMS, ELN, CDS, and SDMS
• Design and optimize SQL and NoSQL data models to support structured, semi-structured, and high-volume scientific data
• Build and support ETL/ELT and next-generation data pipelines for analytics and AI/ML workloads
• Support AI/ML use cases by preparing datasets, enabling feature engineering, and integrating models into pipelines and applications
• Implement and maintain CI/CD pipelines for automated build, testing and deployment
• Apply Test-Driven Development (TDD) practices and develop automated unit, integration, and data validation tests
• Ensure solutions meet performance, data integrity, security, and regulatory compliance requirements (e.g., GxP, 21 CFR Part 11)
• Perform code reviews, debugging, and performance optimization
• Coordinate across cross-functional and geographically distributed teams, managing dependencies and ensuring delivery alignment
• Create ready to deliver technical documentation and track deliverables using JIRA and Confluence

Requirements

• Bachelor’s or master’s degree in computer science, Engineering, Life Sciences with 2–5 years of hands-on experience in Python development with FastAPI (Flask or Django is a plus)
• Proficiency in SQL, including schema design, complex queries, and performance optimization
• Solid understanding of REST APIs, microservices, and integration patterns
• AWS experience, including S3, EC2, Lambda, Step Functions, RDS / Aurora, IAM, monitoring, and logging
• Proficiency with Git-based collaborative development, including branch management, pull requests, code reviews, and integration with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline) to ensure reliable and traceable software delivery
• Hands-on experience with Test-Driven Development and Python testing frameworks such as pytest, unittest, and mocking libraries
• Working knowledge of AI/ML concepts, including data preparation, feature engineering, model integration, and inference workflows
• Exposure to the data and ML libraries such as pandas, NumPy, and scikit-learn (exposure to TensorFlow or PyTorch is a plus)
• Exposure to life sciences, biotech, pharma, or healthcare domains and scientific platforms such as LIMS, ELN, SDMS, CDS, or data lakes
• Ability to design data models aligned to scientific and assay workflows & integrating scientific or enterprise systems and working directly with scientists or lab users
• Knowledge of containerization (Docker) and modern deployment best practices
• Familiarity with Agile/Scrum & SDLC development methodologies
• Strong communication, stakeholder engagement, and cross-team coordination skills.

Tech Stack

• AWS
• Django
• Docker
• EC2
• ETL
• Flask
• Jenkins
• Microservices
• NoSQL
• Numpy
• Pandas
• Python
• PyTorch
• Scikit-Learn
• SDLC
• SQL
• Tensorflow

Benefits

• CURIOSITY DRIVEN, SCIENCE FOCUSED, EMPLOYEE BUILT. Our culture is unlike any other, one where we debate, challenge ourselves, and interact with all alike.
• We are a curious bunch, characterized by our passion to learn and spirit of teamwork.
• We offer a competitive compensation package including accrued vacation, medical, dental, vision, 401k with company matching, life insurance, and flexible spending accounts.
• Zifo is an equal opportunity employer, and we value diversity at our company.