Senior Data Engineer

Morningstar; Toronto; Hybrid; CAD 110,000 - 150,000; Full time

Benefits offered by this job

Hybrid work model

Annual incentive (12.5%)

Job summary

Morningstar is seeking a Senior Data Engineer in Toronto to design and implement scalable end-to-end data pipelines using Snowflake and cloud platforms. You will implement Medallion Architecture, build ingestion pipelines with Python and Informatica, and ensure data quality and performance across environments.

You will collaborate with teams to apply Snowflake AI/ML capabilities, manage version control with Git/Bitbucket, and contribute to CI/CD workflows in a hybrid work model.

Qualifications

• Minimum 5+ years of experience in data engineering or related roles.

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 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.

Skills

Data engineering

Snowflake

Python

SQL

Git/Bitbucket

AWS

CI/CD

AI/ML in Snowflake

Tools

Snowflake

Informatica

Python

Git/Bitbucket

AWS

Job description

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.

Key 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.

Required Skills & Experience

• 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 Have

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

12.5% Annual incentive target percentage.

Morningstar's hybrid work environment gives you the opportunity to collaborate in‑person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in‑office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.

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