Python Developer – Data Engineering

Job Description

Python Developer – Data Engineering | Pandas, Polars, Docker, Kubernetes, Dask

Toronto, ON - Hybrid (4 Days WFO)

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

• Experience with additional Python libraries for data science and machine learning.

• Familiarity with CI/CD pipelines and DevOps practices.

• Background in financial services or capital markets data systems.

Requirements
Java