Python Full Stack Engineer

Job Summary

As a Full Stack Engineer, you will design, build, test, and enhance cloud-native applications that support the R&D organization. Working at the intersection of science and technology, you will collaborate with product owners, data experts, and engineering teams to deliver reliable, high-quality solutions that enable data-driven decision-making.

This role combines full-stack development, cloud engineering, and system design. You will contribute to architecture and engineering practices while building your technical depth and taking ownership of assigned features from development through deployment.

Responsibilities

Engineering & Development

• Design, develop, test, and maintain cloud-native applications across front-end and back-end systems
• Build and enhance services within modern microservice-based and distributed architectures
• Develop and integrate RESTful APIs and system interfaces for enterprise platforms
• Contribute to infrastructure-as-code solutions and cloud environment configuration
• Build, use, and improve CI/CD pipelines for automated deployment and delivery

Architecture & Design

• Contribute to technical design, system architecture, and engineering standards under the guidance of senior engineers
• Create and maintain technical documentation, including Low-Level Designs, Entity-Relationship diagrams, and component or class diagrams
• Work with stakeholders and senior team members to translate business requirements into effective technical solutions

Quality & Reliability

• Develop and maintain automated tests, including unit, integration, and end-to-end tests
• Monitor applications and troubleshoot production issues related to performance and reliability
• Follow security, compliance, and operational standards throughout the software development lifecycle

Collaboration & Delivery

• Work closely with product managers, data subject-matter experts, and cross-functional teams
• Participate in Agile development activities, including sprint planning, code reviews, demonstrations, and retrospectives
• Collaborate with global teams and vendors to deliver assigned features and technical solutions
• Communicate progress, risks, and technical considerations clearly to team members and stakeholders

Data & Platform Integration

• Integrate applications with enterprise R&D systems and scientific data platforms
• Support data ingestion, transformation, access, and application integration
• Contribute to system interoperability using data models, metadata standards, and enterprise integration patterns

Continuous Improvement & Growth

• Contribute ideas that improve development processes, tools, code quality, and maintainability
• Help identify and reduce technical debt
• Use AI-assisted development tools responsibly to improve productivity and software quality
• Build knowledge of cloud-native engineering, R&D platforms, and relevant business domains

Ownership

• Take ownership of assigned features and components from development through deployment and support
• Seek guidance when needed and incorporate feedback from code reviews and technical discussions
• Investigate technical issues, propose solutions, and escalate complex design decisions appropriately

Required Qualifications

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

2+ years of software engineering or application development experience

Technical Skills

• Experience with Python
• Experience with JavaScript
• Experience developing front-end and back-end application components
• Experience with the React JavaScript framework
• Foundational hands-on experience with AWS cloud services, such as EC2, RDS, or S3
• Familiarity with infrastructure-as-code concepts and tools such as Terraform
• Understanding of microservices, cloud-native design, and REST API development

Data & Systems

• Experience working with relational databases, such as Oracle, PostgreSQL, or MySQL
• Familiarity with NoSQL databases, such as DynamoDB or Neptune
• Basic knowledge of data pipelines or data-processing platforms, such as Spark or Databricks
• Understanding of system design fundamentals and data modeling