Python Developer – Data Engineering
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
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- Posting language
- English
- Posted
Original job description
Python Developer – Data Engineering | Pandas, Polars, Docker, Kubernetes, Dask
Role Description
We are seeking a skilled Python Developer to join our data engineering team. You will design, develop, and maintain high-performance data processing pipelines using modern Python frameworks and tools. In this role, youll work with large-scale datasets, containerized systems, and distributed computing platforms to deliver robust data solutions.
Key Responsibilities
• Develop and optimize data manipulation workflows using pandas and polars to handle large datasets efficiently.• Design and implement containerized applications using Docker and Kubernetes to ensure scalable, reliable deployments.
• Build and maintain data pipelines integrating with ClickHouse columnar databases for analytical workloads.
• Develop event-driven architectures using NATS messaging systems for asynchronous data processing.
• Write comprehensive unit tests using pytest to ensure code quality and reliability.
• Implement distributed computing solutions with Dask for processing data beyond single-machine memory constraints.
• Manage version control using Git and collaborate on code repositories following best practices.
Required Skills and Experience
Python & Data Processing: Advanced proficiency in pandas and polars for data manipulation, transformation, and analysis. Experience optimizing code performance for large datasets.
Containerization & Orchestration: Hands-on experience with Docker for building container images and composing multi-container applications. Knowledge of Kubernetes for container orchestration and deployment management.
Data Infrastructure: Working knowledge of ClickHouse or similar columnar databases for OLAP workloads and analytical queries.
Messaging & Streaming: Familiarity with NATS.io for building message-driven systems and asynchronous workflows.
Testing & Quality Assurance: Proficiency with pytest for writing unit tests, integration tests, and maintaining code coverage standards.
Distributed Computing: Experience with Dask for parallel processing and handling out-of-core computations.
Version Control: Strong command of Git workflows, branching strategies, and collaborative development practices.
Preferred Qualifications
• Familiarity with CI/CD pipelines and DevOps practices.
• Background in financial services or capital markets data systems.
More about this job
Responsibilities
Develop and optimize high-performance data processing workflows and pipelines using Python, Pandas, Polars, and ClickHouse. Build containerized and distributed systems with Docker, Kubernetes, NATS, and Dask, while maintaining quality through testing and collaborative Git practices.
Requirements
Requires advanced Python data-processing skills, including Pandas and Polars, and hands-on experience with Docker, Kubernetes, and distributed computing using Dask. Candidates should also have experience with analytical databases, NATS messaging, pytest, and Git; CI/CD, data science, machine learning, or financial-services experience is preferred.
Skills
- Python
- Pandas
- Polars
- Docker
- Kubernetes
- ClickHouse
- NATS
- Pytest
- Dask
- Git
- Data Pipelines
- Distributed Computing
- Containerization
- Data Processing
- CI/CD
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Software
- Technology
- Finance & Accounting
Keywords
- Python
- Pandas
- Polars
- Docker
- Kubernetes
- ClickHouse
- NATS
- Pytest
- Dask
- Git
- Data Engineering
- Data Processing
- Data Pipelines
- Large-Scale Datasets
- Containerization
- Distributed Computing
- Parallel Processing
- Out-Of-Core Computation
- OLAP
- Analytical Queries
- Event-Driven Architecture
- Asynchronous Processing
- Unit Testing
- Integration Testing
- Code Coverage
- CI/CD
- DevOps
- Data Science
- Machine Learning
- Financial Services