RQ09731 - Sr. DataOps/Cloud Data Engineer

RQ09731 - Sr. DataOps/Cloud Data Engineer

Downtown, Toronto (Jarvis St.)

Hybrid: 3 Days onsite / 2 days remote

- From October 20, 2025, the candidate is required to work onsite 4 days a week and 1 day from home

- From January 5, 2026, the candidate is required to work onsite 5 days a week fully

Contract (6+ months, possible extension)

2 Openings

• Designing and developing data pipelines from source to end user
• Optimizing data pipelines
• Review business requirements, familiarize with and understand business rules and transactional source data model
• Review performance of Extract Load Transform (ELT) pipelines with developers and suggest improvements
• Create end-to-end integration tests for data pipelines and improve pipelines accordingly
• Translate requirements into clear design specifications, and present solutions for team review, incorporating feedback and direction from team lead and team members in a collaborative development environment.
• Conduct Knowledge Transfer and training sessions, ensuring staff receive the required knowledge to support and improve upon the system. Develop learning activities using review-watch-do methodology & demonstrate the ability to prepare and present.
• Develop documentation and materials as part of a review and knowledge transfer to other team members

Must-Have:

- Demonstrated fluency in Python, with knowledge of its best practices, coding conventions, and application in building robust, scalable data pipelines.

- Experience with Data pipeline and workflow development, orchestration, deployment, and automation and specializing in programming and pipelines to create and managing the dataflow and movement of data.

- Experience with Cloud data platforms, data management and data exchange tools and technologies.

- Experience with Commercial and open-source data and database development and management and specializing in data storage setting and managing cloud Data As a Service (DaaS), application Database as a Service (DBaaS), Data Warehouses as a Service (DWaaS), and other storage platforms (both in the cloud and on-premise).

- Understanding and working knowledge of iterative product development cycles (Discovery, Agile, Beta, Live)

- Experience contributing to version-controlled, shared codebases using git (Azure DevOps, GitHub, Bitbucket) and participating in pull request code reviews.

- Experience in Continuous Integration/Continuous Development/Deployment (CI/CD) and Data provisioning Automation.

- Proficiency in SQL and Python, with hands-on experience using Databricks and Spark SQL for data modelling and transformation tasks.