Senior Data Engineer

MorningstarToronto, ON
Hybrid

About The Position

We are looking for a Senior Data Engineer with a minimum of 5+ years of experience in designing and implementing scalable, end-to-end data engineering solutions. The ideal candidate should have strong hands-on expertise in Snowflake, modern data pipeline development, cloud platforms, version-controlled development practices, and AI-enabled data solutions.

Requirements

  • Minimum 5+ years of experience in data engineering or related roles.
  • Strong hands-on experience with Snowflake, including data modeling, performance tuning, and production workloads.
  • Proven experience in building end-to-end data pipelines.
  • Strong proficiency in Python for data processing and automation.
  • Experience with Informatica or similar ETL tools.
  • Advanced SQL skills with strong understanding of data warehousing concepts.
  • Hands-on experience with Snowflake AI/ML capabilities (e.g., Snowflake Cortex, LLM-based features, or similar AI-driven data platforms).
  • Experience using Git / Bitbucket for version control in collaborative environments.
  • Basic to intermediate understanding of CI/CD pipelines and deployment workflows.
  • Hands-on experience with AWS cloud services and cloud-based data architectures.

Nice To Haves

  • Exposure to Agile/Scrum development environments.
  • Knowledge of Terraform / Infrastructure as Code.
  • Exposure to Tableau or other BI / data visualization tools.

Responsibilities

  • Design, build, and maintain end-to-end data pipelines with Snowflake as the primary data warehouse.
  • Implement and support Medallion Architecture within Snowflake for structured and scalable data management.
  • Develop data ingestion pipelines using Python and/or Informatica to load data into Snowflake.
  • Build scalable and efficient data models and transformation logic within Snowflake.
  • Leverage Snowflake AI/ML capabilities (e.g., Snowflake Cortex, AI functions, or LLM integrations where applicable) to enable advanced analytics, automation, and data-driven insights.
  • Manage SQL scripts, Python code, and Snowflake objects in a structured and maintainable way.
  • Use Git / Bitbucket for version control of Snowflake scripts, Python code, and deployment artifacts.
  • Support and implement CI/CD and deployment processes for data pipelines and database changes.
  • Monitor, troubleshoot, and optimize data pipelines across all environments.
  • Ensure data quality, consistency, and performance across all pipelines.

Benefits

  • Hybrid work environment
  • Tools and resources to engage meaningfully with global colleagues
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