Pebl

Pebl

Analytics Engineer

Toronto, Ontario

Sponsorship not specifiedDetected 43 days ago
PythonCode ReviewSQLSnowflakeAWSCloud PlatformsCI/CDAirflowdbtData EngineeringLeadership

About the role

  • Purpose in Every Position Pebl puts a world of talent at your fingertips.
  • Backed by more than a decade of compliance leadership and local expertise, Pebl helps businesses move fast, stay compliant, and scale with confidence.

Responsibilities

  • Implement and maintain data pipelines for analytics and operational workflows using SQL and Python
  • Design and develop data warehouse models, tables, and schemas to support scalable and maintainable analytics solutions
  • Build and sustain workflows with Airflow (Astronomer) for efficient data orchestration
  • Collaborate with teams to implement data solutions aligned with our needs
  • Support data governance practices and handle PII and sensitive data in cloud environments
  • We power global teams and believe diverse perspectives drive innovation and impact.

Requirements

  • Hands-on experience with dbt and data modeling techniques
  • Proficiency in SQL and Python for data processing and pipeline development
  • Let's Connect If You Have:

Skills

  • Purpose in Every Position
  • Pebl puts a world of talent at your fingertips.
  • With Pebl, companies everywhere can hire great talent anywhere.
  • Where Your Work Moves the Needle.
  • What Makes You a Great Fit

Benefits

  • Flexible Time Off - Take the time you need to recharge.
  • Parental Leave - Support for growing families.
  • Health and Dental Insurance - Where applicable, to cover you and your loved ones.
  • Retirement Savings + Employee Incentive Plan - Plan for the future while sharing our success.

Company info

  • At Pebl, we're committed to supporting our team with comprehensive rewards and benefits designed to meet diverse needs across roles and locations.

Equal opportunity

  • Pebl is an Equal Opportunity Employer.

This listing is sourced directly from Pebl's careers page and normalized into a canonical job model.