Who you are

- We are seeking engineers who bring deep analytical rigor to data modeling and relentless dedication to data quality

- Solid Academic &
- Technical Foundation: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)

- Early Career Momentum: 2-3 years of hands-on experience in data engineering, BI engineering, or equivalent project-driven environments

- SQL Mastery: Exceptional ability to write clean SQL (joins, filters, aggregations) and query across cloud data platforms like Snowflake, BigQuery, or Databricks

- ETL/ELT Aptitude: Strong foundational understanding of data transformation concepts and comfort navigating tools like dbt and Airflow under guided supervision

- Code &

- Version Control Literacy: Familiarity with Python (or similar languages) and Git-based collaborative workflows to ship clean code efficiently

What the job involves

- As our Data Engineer I, you will build and fine-tune the robust cloud data pipelines that power our analytics, data science, and product teams

- You’ll tackle real-world architecture challenges, collaborate with world-class engineers,



and directly shape how intelligent learning experiences reach millions of users worldwide

- Engineer the Flow: Implement high-performance ELT/ETL transformations using tools like dbt based on cutting-edge specs from senior engineers and architects

- Orchestrate Data Jobs: Build, deploy, and maintain seamless data ingestion and transformation jobs powered by Airflow and Cloud Composer

- Guard Data Quality: Write and execute vital data tests—including row counts, null checks, and reference validations—to keep our datasets clean and trusted

- Monitor &
- Resolve: Proactively investigate automated alerts, ensuring peak system performance and escalating deeper architectural changes when needed

- Shape Lakehouse Architecture: Maintain pristine schemas and naming conventions across Snowflake, BigQuery, or Databricks while implementing table and view enhancements

- Document &

- Empower: Create clear documentation for pipelines, schemas, and caveats so the entire engineering team can safely build on your foundation

- Partner &

- Collaborate: Work closely alongside Analytics and Data Science teams to deeply understand their query needs and deliver optimized data structures

📌 Data Engineer (Toronto)
🏢 Docebo
📍 Toronto