
Data AI Engineering Specialist (Hybrid)
- Montreal, QC
- Permanent
- Full-time
- Develop and maintain data pipelines and ETL (Extract, Transform, Load) processes.
- Work with structured and unstructured data to ensure it is accessible and usable.
- Optimize data systems for performance and scalability.
- Implement data quality and data governance standards.
- Collaborate with stakeholders across technology and business units to understand their data needs and translate them into technical solutions and provide data-driven insights.
- Contribute to the documentation and knowledge sharing within the team, creating, and maintaining technical documentation and training materials.
- Participate in code reviews and contribute to the improvement of development processes.
- Contribute to the broader data architecture community through knowledge sharing, presentations.
- 8 years+ of being a practitioner in data engineering or a related field.
- Proficiency in programming skills in Python
- Experience with data processing frameworks like Apache Spark or Hadoop.
- Knowledge of database systems (SQL and NoSQL).
- Experience working on Snowflake and Databricks.
- Experience on Snowflake Cortex will be really appreciated.
- Familiarity with cloud platforms (AWS, Azure) and their data services.
- Understanding of data modeling and data architecture principles.
- Experience with data warehousing concepts and technologies.
- Experience with message queues and streaming platforms (e.g., Kafka).
- Experience with version control systems (e.g., Git).
- Experience using Jupyter notebooks for data exploration, analysis, and visualization.
- Excellent communication and collaboration skills.
- Ability to work independently and as part of a geographically distributed team.
- Familiarity with data visualization tools (e.g., Tableau, Power BI).
- Familiarity with data governance and security best practices (e.g., data access control, data masking).
- Experience with Agile methodologies.
- Familiarity with data catalog and metadata management tools (e.g., Collibra).
- Familiarity with CI/CD pipelines and DevOps practices.