DataOps Engineer (DevOps for Data)

Role Overview

You’ll join the team as a DataOps Engineer (DevOps for Data) to optimize a cloud-based data platform. The role focuses on building reliable DataOps CI/CD automation, integrating key data platforms, and improving monitoring, documentation, and adoption.

Responsibilities

• Implement and maintain DataOps CI/CD pipelines for fast, reliable deployment of infrastructure and data products, including:
  • Automated environment provisioning and management
  • Testing and linting stages
• Collaborate with IT and Analytics Engineering to implement and continuously improve monitoring and alerting, especially within CI/CD pipelines, to proactively identify and resolve issues.
• Drive automation of documentation for CI/CD pipelines and operating procedures; measure adoption and identify continuous improvement opportunities.
• Establish critical integrations between platforms to support scalable data platform operations.
• Apply security best practices, including security testing within pipelines.

Requirements

• Strong understanding of DataOps/DevOps principles and experience on teams managing data as a product.
• Experience integrating core data platforms and orchestration tools into CI/CD automations, including Snowflake, dbt, Airflow, and Fivetran.
• 4+ years hands-on experience implementing and managing cloud-native data platforms (with an additional 2+ years qualification noted for candidates with a Master’s degree).
• Proficiency in deployment strategies, environment management, and testing best practices for data products, with an emphasis on automation and quality.
• Ability to debug and troubleshoot failed tests and CI/CD pipeline issues.
• Familiarity with data modeling, ETL/ELT, and data warehousing best practices.
• 4+ years practical experience applying DataOps (DevOps for data) principles.
• Experience applying Agile methodologies to DataOps/DevOps work.
• 4+ years experience building and maintaining robust CI/CD pipelines for data platforms/products.
• Excellent communication and collaboration with technical and non-technical stakeholders.

Nice to Have

• Experience with data cataloging, lineage, and data quality tooling such as Atlan or Acceldata.

Location / Work Style

• Full remote, with openness to ~50% reporting to the Toronto office.