Job Description
eDNA Explorer is expanding through partnership with Dr. Caren Helbing’s laboratory at the University of Victoria to create eDNA Explorer Canada! We are building a cutting-edge platform for processing and analyzing environmental DNA (eDNA) data. Our system processes biological samples to identify species based on their genetic material, integrates environmental data, and provides insights into biodiversity and ecological patterns. We're using modern cloud-native data engineering principles to build robust, scalable pipelines for scientific data analysis.
We're seeking a Full-stack Engineer to enhance and maintain our comprehensive eDNA Explorer Canada platform, which includes both cutting-edge web applications and scientific data processing systems. This role involves building sophisticated data visualization components, implementing complex user workflows, developing type-safe APIs, and maintaining Python-based data processing pipelines and report generation services.
The ideal candidate will have strong React/TypeScript experience with a passion for creating intuitive interfaces for complex scientific data, combined with solid Python backend development skills for data-intensive applications.
Our platform consists of:
Front-end Web Applications: Modern React-based interfaces for scientific data analysis and research collaboration
Python Data Processing Services: Flask-based APIs and report generation systems handling large-scale scientific datasets
Data Pipeline Infrastructure: Dagster-based workflows for processing genomic and environmental data
Requirements
Core Experience (Required)
4+ years of full-stack web development experience
Strong experience with React 18+ and TypeScript
Solid understanding of Next.js (App Router and Pages Router)
Experience with Python web development using Flask or FastAPI
Knowledge of modern database technologies (PostgreSQL, SQLAlchemy)
Experience with tRPC for type-safe APIs
Familiarity with modern testing frameworks (Vitest, Playwright, React Testing Library, pytest)
Preferred Experience
Component-driven development and design systems
Understanding of monorepo architecture and Turborepo (for TS) and Poetry (for Python)
Knowledge of cloud services and deployment pipelines (Google Cloud Platform preferred)
Experience with data visualization libraries and scientific applications
Background in Redis/RQ for job queuing systems
Experience with scientific data processing or bioinformatics applications
Knowledge of containerization (Docker) and orchestration (Kubernetes)
Experience with AI-powered development tools like Claude Code, GitHub Copilot, or similar agentic coding assistants
Familiarity with AI frameworks such as Google AI SDK or PydanticAI (a plus)
Technology Stack
Front-end Technologies
React & Next.js: React 19 with functional components and hooks, Next.js 15 with both App Router and Pages Router patterns
TypeScript: Comprehensive type safety across the entire application
React 19 compatibility: With React Compiler integration
UI & Styling: Custom component library (@cal-edna/ui) with Storybook documentation, Tailwind CSS for utility-first styling
State Management: Zustand for client state, tRPC for server state management
Data Fetching: tRPC for type-safe API calls with automatic TypeScript generation
Forms: React Hook Form with Zod validation for type-safe form handling
Testing: Vitest for unit testing, Playwright for E2E testing, React Testing Library for component testing
Back-end Technologies
Python Web Frameworks: Flask 3.0+ for API services, with potential FastAPI integration
Database: PostgreSQL with SQLAlchemy 2.0+ ORM for robust data modeling
Job Processing: Redis with RQ (Redis Queue) for background job processing
Authentication: Experience with JWT-based authentication
Cloud Services: Google Cloud Platform (BigQuery, Cloud Storage, Secret Manager)
Data Visualization: Plotly for interactive scientific visualizations
Containerization: Docker with Kubernetes deployment
Data Processing: polars for scientific data manipulation
Scientific Computing: scipy, scikit-bio, scikit-learn for data analysis
Development & Infrastructure
Monorepo Architecture: Turborepo for efficient builds and dependency management
Package Management: yarn for frontend, Poetry for Python
Version Control: Git with conventional commits
CI/CD: GitHub Actions with automated testing and deployment
Code Quality: ESLint, Prettier, Ruff (Python), pre-commit hooks
Documentation: Storybook for component documentation, comprehensive API documentation
Data Processing Pipeline
Workflow Orchestration: Dagster for data pipeline management
Data Storage: Google Cloud Storage, BigQuery for large-scale data analytics
Data Formats: Support for scientific data formats (FASTA, TSV, compressed formats)
Performance Optimization: Polars for high-performance data processing
Key Responsibilities
Front-end Development
Build and maintain React applications for scientific data visualization and analysis
Develop reusable UI components following design system principles
Implement complex data visualization dashboards using modern charting libraries
Create intuitive user workflows for researchers and scientists
Ensure type safety across the entire frontend application stack
Optimize application performance for large scientific datasets
Back-end Development
Design and implement Flask APIs for data processing and report generation
Manage database operations using SQLAlchemy for complex scientific data models
Develop background job processing systems using Redis and RQ
Build report generation services that process large-scale genomic and environmental data
Integrate with Google Cloud services for scalable data processing
Implement robust authentication and authorization systems
System Integration
Connect frontend applications with Python backend services via tRPC
Maintain data consistency across web applications and processing pipelines
Optimize system performance for handling large scientific datasets
Implement monitoring and logging for both web and data processing components
Ensure security best practices across the entire platform
Data & Analytics
Work with scientific datasets including genomic sequences, environmental data, and biodiversity information
Implement data validation and quality assurance processes
Build interactive dashboards for scientific data exploration
Create data export and download functionality for researchers
What You'll Build
Web Applications
Interactive data visualization dashboards for biodiversity analysis
Real-time data processing interfaces with progress tracking
Complex form systems for scientific metadata collection
Responsive data tables with advanced filtering and sorting
Map-based visualizations for geographic species distribution
Backend Services
Report generation APIs that process terabytes of scientific data
Background job systems for long-running data processing tasks
Data validation services for scientific metadata
Authentication and user management systems
File processing and storage services for scientific datasets
Integration Features
Real-time updates between web interfaces and data processing jobs
Type-safe API contracts between frontend and backend systems
Scalable file upload and processing workflows
Advanced search and filtering across scientific datasets
Technical Challenges
Performance optimization for applications handling large scientific datasets
Complex state management across multiple interconnected applications
Real-time updates for long-running scientific computations
Type safety across full-stack applications with complex data models
Scientific data visualization with interactive and responsive charts
Scalable architecture supporting growing research community
Team & Culture
You'll join a collaborative international team of scientists, engineers, and researchers working on meaningful environmental and biodiversity research. Our development culture emphasizes:
AI-native development leveraging modern coding assistants and tools for enhanced productivity
Code quality and testing with comprehensive test coverage
Type safety and robust error handling across all systems
Performance and scalability for scientific computing workloads
Documentation and knowledge sharing for complex scientific processes
Collaborative problem-solving with domain experts and researchers
Continuous learning and adoption of cutting-edge development tools and practices
Growth Opportunities
Scientific domain expertise in environmental biology and genomics
Advanced data engineering and pipeline optimization
Cloud architecture and distributed systems design
Open-source contributions to scientific computing tools
Research collaboration with academic institutions and environmental organizations
Benefits
This is a grant-funded position with the possibility of future hiring as an employee at the end of the grant.
eDNA Explorer Canada is committed to building a diverse team. We encourage applications from candidates of all backgrounds.
This position is available as remote within Canada with preference for candidates who can occasionally visit our offices located at the University of Victoria on Vancouver Island in beautiful British Columbia. Applicant must be a Canadian citizen or have a valid work permit to work in Canada.
The Helbing lab is situated in the Department of Biochemistry & Microbiology at the University of Victoria. The eDNA Explorer platform can be viewed here: https://www.ednaexplorer.org.
We're looking for engineers who are excited about building tools that enable groundbreaking environmental research that can truly change the world. If you're passionate about creating robust, scalable applications that help scientists understand and protect biodiversity, we'd love to hear from you.
This role offers the unique opportunity to work at the intersection of modern web development and cutting-edge environmental science, building tools that have real impact on our understanding of the natural world.
We're seeking a Full-stack Engineer to enhance and maintain our comprehensive eDNA Explorer Canada platform, which includes both cutting-edge web applications and scientific data processing systems. This role involves building sophisticated data visualization components, implementing complex user workflows, developing type-safe APIs, and maintaining Python-based data processing pipelines and report generation services.
The ideal candidate will have strong React/TypeScript experience with a passion for creating intuitive interfaces for complex scientific data, combined with solid Python backend development skills for data-intensive applications.
Our platform consists of:
Front-end Web Applications: Modern React-based interfaces for scientific data analysis and research collaboration
Python Data Processing Services: Flask-based APIs and report generation systems handling large-scale scientific datasets
Data Pipeline Infrastructure: Dagster-based workflows for processing genomic and environmental data
Requirements
Core Experience (Required)
4+ years of full-stack web development experience
Strong experience with React 18+ and TypeScript
Solid understanding of Next.js (App Router and Pages Router)
Experience with Python web development using Flask or FastAPI
Knowledge of modern database technologies (PostgreSQL, SQLAlchemy)
Experience with tRPC for type-safe APIs
Familiarity with modern testing frameworks (Vitest, Playwright, React Testing Library, pytest)
Preferred Experience
Component-driven development and design systems
Understanding of monorepo architecture and Turborepo (for TS) and Poetry (for Python)
Knowledge of cloud services and deployment pipelines (Google Cloud Platform preferred)
Experience with data visualization libraries and scientific applications
Background in Redis/RQ for job queuing systems
Experience with scientific data processing or bioinformatics applications
Knowledge of containerization (Docker) and orchestration (Kubernetes)
Experience with AI-powered development tools like Claude Code, GitHub Copilot, or similar agentic coding assistants
Familiarity with AI frameworks such as Google AI SDK or PydanticAI (a plus)
Technology Stack
Front-end Technologies
React & Next.js: React 19 with functional components and hooks, Next.js 15 with both App Router and Pages Router patterns
TypeScript: Comprehensive type safety across the entire application
React 19 compatibility: With React Compiler integration
UI & Styling: Custom component library (@cal-edna/ui) with Storybook documentation, Tailwind CSS for utility-first styling
State Management: Zustand for client state, tRPC for server state management
Data Fetching: tRPC for type-safe API calls with automatic TypeScript generation
Forms: React Hook Form with Zod validation for type-safe form handling
Testing: Vitest for unit testing, Playwright for E2E testing, React Testing Library for component testing
Back-end Technologies
Python Web Frameworks: Flask 3.0+ for API services, with potential FastAPI integration
Database: PostgreSQL with SQLAlchemy 2.0+ ORM for robust data modeling
Job Processing: Redis with RQ (Redis Queue) for background job processing
Authentication: Experience with JWT-based authentication
Cloud Services: Google Cloud Platform (BigQuery, Cloud Storage, Secret Manager)
Data Visualization: Plotly for interactive scientific visualizations
Containerization: Docker with Kubernetes deployment
Data Processing: polars for scientific data manipulation
Scientific Computing: scipy, scikit-bio, scikit-learn for data analysis
Development & Infrastructure
Monorepo Architecture: Turborepo for efficient builds and dependency management
Package Management: yarn for frontend, Poetry for Python
Version Control: Git with conventional commits
CI/CD: GitHub Actions with automated testing and deployment
Code Quality: ESLint, Prettier, Ruff (Python), pre-commit hooks
Documentation: Storybook for component documentation, comprehensive API documentation
Data Processing Pipeline
Workflow Orchestration: Dagster for data pipeline management
Data Storage: Google Cloud Storage, BigQuery for large-scale data analytics
Data Formats: Support for scientific data formats (FASTA, TSV, compressed formats)
Performance Optimization: Polars for high-performance data processing
Key Responsibilities
Front-end Development
Build and maintain React applications for scientific data visualization and analysis
Develop reusable UI components following design system principles
Implement complex data visualization dashboards using modern charting libraries
Create intuitive user workflows for researchers and scientists
Ensure type safety across the entire frontend application stack
Optimize application performance for large scientific datasets
Back-end Development
Design and implement Flask APIs for data processing and report generation
Manage database operations using SQLAlchemy for complex scientific data models
Develop background job processing systems using Redis and RQ
Build report generation services that process large-scale genomic and environmental data
Integrate with Google Cloud services for scalable data processing
Implement robust authentication and authorization systems
System Integration
Connect frontend applications with Python backend services via tRPC
Maintain data consistency across web applications and processing pipelines
Optimize system performance for handling large scientific datasets
Implement monitoring and logging for both web and data processing components
Ensure security best practices across the entire platform
Data & Analytics
Work with scientific datasets including genomic sequences, environmental data, and biodiversity information
Implement data validation and quality assurance processes
Build interactive dashboards for scientific data exploration
Create data export and download functionality for researchers
What You'll Build
Web Applications
Interactive data visualization dashboards for biodiversity analysis
Real-time data processing interfaces with progress tracking
Complex form systems for scientific metadata collection
Responsive data tables with advanced filtering and sorting
Map-based visualizations for geographic species distribution
Backend Services
Report generation APIs that process terabytes of scientific data
Background job systems for long-running data processing tasks
Data validation services for scientific metadata
Authentication and user management systems
File processing and storage services for scientific datasets
Integration Features
Real-time updates between web interfaces and data processing jobs
Type-safe API contracts between frontend and backend systems
Scalable file upload and processing workflows
Advanced search and filtering across scientific datasets
Technical Challenges
Performance optimization for applications handling large scientific datasets
Complex state management across multiple interconnected applications
Real-time updates for long-running scientific computations
Type safety across full-stack applications with complex data models
Scientific data visualization with interactive and responsive charts
Scalable architecture supporting growing research community
Team & Culture
You'll join a collaborative international team of scientists, engineers, and researchers working on meaningful environmental and biodiversity research. Our development culture emphasizes:
AI-native development leveraging modern coding assistants and tools for enhanced productivity
Code quality and testing with comprehensive test coverage
Type safety and robust error handling across all systems
Performance and scalability for scientific computing workloads
Documentation and knowledge sharing for complex scientific processes
Collaborative problem-solving with domain experts and researchers
Continuous learning and adoption of cutting-edge development tools and practices
Growth Opportunities
Scientific domain expertise in environmental biology and genomics
Advanced data engineering and pipeline optimization
Cloud architecture and distributed systems design
Open-source contributions to scientific computing tools
Research collaboration with academic institutions and environmental organizations
Benefits
This is a grant-funded position with the possibility of future hiring as an employee at the end of the grant.
eDNA Explorer Canada is committed to building a diverse team. We encourage applications from candidates of all backgrounds.
This position is available as remote within Canada with preference for candidates who can occasionally visit our offices located at the University of Victoria on Vancouver Island in beautiful British Columbia. Applicant must be a Canadian citizen or have a valid work permit to work in Canada.
The Helbing lab is situated in the Department of Biochemistry & Microbiology at the University of Victoria. The eDNA Explorer platform can be viewed here: https://www.ednaexplorer.org.
We're looking for engineers who are excited about building tools that enable groundbreaking environmental research that can truly change the world. If you're passionate about creating robust, scalable applications that help scientists understand and protect biodiversity, we'd love to hear from you.
This role offers the unique opportunity to work at the intersection of modern web development and cutting-edge environmental science, building tools that have real impact on our understanding of the natural world.
💡 Quick Summary
Seeking a career-building opportunity? The Senior Software Engineer position is now open for candidates interested in the Back Office Jobs sector. This role in British Columbia offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Back Office Jobs is a plus.
