Full Job Description
Role:Python-Data Engineer
Location: Toronto , ON
Mandatory skills:Python (Expert), Docker & Kubernetes (Medium), CI/CD pipeline (Medium), Pandas & Polars (Expert)
Job 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, you'll 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.
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.
Compensation: We are offering between $75,000 - $90,000. Applications will be accepted until Oct 9, 2026.Cognizant will only consider applicants for this position who are legally authorized to work in Canada without requiring employer sponsorship, now or at any time in the future.
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
