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Added 1 min ago
Senior Java Modernization Engineer
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Full-time
Added 1 min ago
Millwork Engineer / CNC Programmer
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Added 32 mins ago
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Added 32 mins ago
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Added 46 mins ago
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Added 46 mins ago
Senior Building Operator
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Added 46 mins ago
Aircraft Maintenance Engineer (M1 Licenced)
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Added 1 hour ago
DevOps Manager
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Added 1 hour ago
Engineer (Electrical Power) - High Current Lab
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Toronto, ON
Full-time
Position: Engineer/Analyst (Electrical Power) - High Current Lab

Cognizant

Python-Data Engineer

Toronto, ON, Canada

Pay: Compensation: We are offering between $75,000 – $90,000.
Job type: Full-time
Apply by: Oct 28, 2026
Posting language: English
Posted: Sep 28, 2026

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Original job description

About The Group:

Cognizant’s Cloud, Infrastructure, and Security Services Practice (CIS), is all about accepting digital transformation by driving core modernization holistically across layers. We help customers transform infrastructure and workplace to meet the constantly evolving needs of the digital era. Our broad approach delivers key results for our customers by achieving cloud driven modernization and workplace and operational transformation to own the business in a secure environment.

Please note, this role is not able to offer visa transfer or sponsorship now or in the future*

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.

*Please note, this role is not able to offer visa transfer or sponsorship now or in the future*

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More about this job

Responsibilities

Develop and optimize high-performance data processing workflows and pipelines using Python, pandas, and polars, integrating with ClickHouse and NATS. Build containerized and distributed data solutions with Docker, Kubernetes, and Dask, while testing with pytest and collaborating through Git.

Requirements

Requires advanced Python, pandas, and polars skills, along with experience handling large datasets and optimizing performance. Candidates should have working knowledge of Docker, Kubernetes, ClickHouse or similar columnar databases, NATS, pytest, Dask, and Git; CI/CD familiarity and financial services experience are preferred.

Skills

Python
Pandas
Polars
Docker
Kubernetes
CI/CD
ClickHouse
NATS
Pytest
Dask
Git
Data Pipelines
Distributed Computing
Data Processing
Containerization

Visa sponsorship

Not detected in the job text

Keywords

Python
Pandas
Polars
Docker
Kubernetes
CI/CD
ClickHouse
NATS
Pytest
Dask
Git
Data Engineering
Data Pipelines
Data Processing
Large-Scale Datasets
Distributed Computing
Out-of-Core Computation
Containerization
Container Orchestration
Event-Driven Architecture
Asynchronous Processing
Columnar Databases
OLAP
Unit Testing
Integration Testing
Code Coverage
Version Control
DevOps
Financial Services
Capital Markets

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