Data Engineer I - Cloud Data Pipelines

Docebo; Biassono; Hybrid; EUR 29,000 - 39,000; Full time

Benefits offered by this job

ESPP 15% discount on stock purchase

Health benefits

Paid vacation days

Docebo Days

Floating holidays

Birthday off

Meal voucher

Job summary

Docebo is actively seeking a Data Engineer I to design and optimize robust cloud data pipelines powering analytics, data science, and product teams worldwide.

You will tackle real-world architecture challenges, collaborate with engineers, and shape how intelligent learning experiences reach millions of users. The role emphasizes data quality, modeling, and collaboration with Analytics/Data Science teams.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
• 2–3 years of hands-on data engineering, BI engineering, or equivalent project-driven environments.
• Proficient in SQL and cloud data platforms (Snowflake/BigQuery/Databricks); strong ETL/ELT understanding.

Responsibilities

• Engineer high-performance ELT/ETL transformations (dbt based) per senior specs.
• Build, deploy, and maintain data ingestion/transformation jobs with Airflow.
• Write data tests (quality, counts, nulls, reference checks) to keep datasets clean.
• Investigate alerts, ensure peak performance, raise architectural changes when needed.
• Maintain lakehouse schemas and naming conventions across Snowflake/BigQuery/Databricks.

Skills

SQL Mastery

ETL/ELT

Python

Git workflows

Data modeling

Data quality

Education

Bachelor's degree in Computer Science, Engineering, Mathematics

Tools

dbt

Airflow

Snowflake

BigQuery

Databricks

Job description

Docebo is actively seeking a Data Engineer I to design and optimize robust cloud data pipelines powering analytics, data science, and product teams worldwide.

You will tackle real-world architecture challenges, collaborate with engineers, and shape how intelligent learning experiences reach millions of users. The role emphasizes data quality, modeling, and collaboration with Analytics/Data Science teams.