Python Data Engineer - Scalable Data Pipelines & Cloud Tech

Cognizant; Toronto; On-site; CAD 75,000 - 90,000; Full time

Job summary

Cognizant’s Cloud, Infrastructure, and Security Services Practice (CIS) is hiring a Python-Data Engineer in Toronto, ON. You will design, develop, and optimize high-performance data pipelines, containerized applications, and analytics workflows using modern Python tools and distributed systems.

You will work with pandas/polars, Docker/Kubernetes, and ClickHouse, while implementing event-driven architectures with NATS and testing with pytest.

Qualifications

• Advanced proficiency in pandas and polars for data manipulation and analysis.
• Experience building scalable data pipelines in containerized environments.
• Familiarity with OLAP databases and distributed processing systems.
• Experience with CI/CD pipelines and DevOps practices.

Responsibilities

• Develop and optimize data manipulation workflows using pandas and polars for large datasets.
• Design and implement containerized applications using Docker and Kubernetes for scalable deployments.
• Build and maintain data pipelines with ClickHouse for analytical workloads.
• Develop event-driven architectures using NATS for asynchronous data processing.
• Write comprehensive unit tests with pytest to ensure code quality.
• Implement distributed computing solutions with Dask for out-of-core processing.

Skills

Python data processing

Containerization

Orchestration

Data infrastructure

Testing & QA

Distributed computing

Git workflows

Tools

Docker

Kubernetes

ClickHouse

NATS.io

pytest

Dask

Git

Job description

Cognizant’s Cloud, Infrastructure, and Security Services Practice (CIS) is hiring a Python-Data Engineer in Toronto, ON. You will design, develop, and optimize high-performance data pipelines, containerized applications, and analytics workflows using modern Python tools and distributed systems.

You will work with pandas/polars, Docker/Kubernetes, and ClickHouse, while implementing event-driven architectures with NATS and testing with pytest.