Data Engineer I: Cloud ELT & Pipelines

Docebo; Toronto; Hybrid; CAD 69,000 - 92,000; Full time

Benefits offered by this job

Health benefits

Paid vacation days

Docebo Days off (two company-wide)

Birthday off

ESPP 15% discount

Job summary

Docebo is hiring a Data Engineer I to build robust cloud data pipelines powering analytics, data science, and product teams. You will collaborate with world-class engineers and help scale data infrastructure for millions of users.

You’ll tackle data modeling, quality, and performance challenges, ensuring reliable data flow. The role emphasizes hands-on coding, teamwork, and data-driven decision making in a hybrid setup.

Qualifications

• Bachelor's degree in CS/Engineering/Math or related field.
• 2–3 years of hands-on data engineering or BI engineering experience.
• Strong SQL skills across cloud platforms (Snowflake, BigQuery, Databricks).
• Experience with Python and Git-based workflows.
• Familiarity with dbt and Airflow is a plus.

Responsibilities

• Implement high-performance ELT/ETL transformations with dbt per engineering specs.
• Build, deploy, and maintain data ingestion and transformation jobs with Airflow/Cloud Composer.
• Write data tests (row counts, null checks, reference checks) to ensure data quality.
• Monitor alerts and coordinate with teams to optimize data pipelines.
• Maintain lakehouse schemas and naming conventions across Snowflake, BigQuery, Databricks.
• Document pipelines, schemas, and caveats for the engineering team.
• Collaborate with Analytics and Data Science to meet query needs.

Skills

SQL mastery

ETL/ELT

Python

Airflow

Git

Data modeling

Cloud data platforms

Education

Bachelor's degree in CS/Engineering/Math

Tools

dbt

Airflow

Cloud Composer

Snowflake

BigQuery

Databricks

Job description

Docebo is hiring a Data Engineer I to build robust cloud data pipelines powering analytics, data science, and product teams. You will collaborate with world-class engineers and help scale data infrastructure for millions of users.

You’ll tackle data modeling, quality, and performance challenges, ensuring reliable data flow. The role emphasizes hands-on coding, teamwork, and data-driven decision making in a hybrid setup.