Tasks and responsibilities - Design, develop, and optimize ETL and ELT data pipelines for structured and unstructured data;

- Integrate data from multiple sources into centralized platforms, including data lakes and data warehouses;
- Develop and maintain logical and physical data models to meet analytical, artificial intelligence, and reporting needs;
- Optimize the performance, scalability and reliability of data systems;
- Implement validation, cleansing, and monitoring processes to ensure data quality, consistency, and compliance;
- Collaborate with data scientists, artificial intelligence engineers, and business teams to deliver reliable and structured data;
- Develop reusable components, APIs, and automation scripts to optimize data flows;
- To ensure data security, privacy, and regulatory compliance;
- Document data pipelines, templates, processes, and best practices;
- Conduct a technology watch to evaluate new tools, cloud technologies and best practices in data engineering.

Qualifications - Bachelor's degree in software engineering, computer science, or master's degree in computer science;

- Possess a minimum of six (6) years of experience in a role related to data engineering, MLOps, software development, or machine learning;
- Master Python;
- Experience with AWS or Azure cloud platforms;




- experience with Google Cloud Platform (GCP) is an asset;
- Possess knowledge of machine learning and an understanding of the associated mathematical foundations;
- Master Linux environments and containerization technologies, including Docker;
- Be comfortable with the Microsoft setting;
- Actively contribute to the continuous improvement of internal processes;
- Participate in the deployment and operationalization of machine learning solutions;
- Experience with MLOps tools and practices (an asset);
- Understanding the principles of computer networking (an asset);
- Have experience in data engineering, ETL, MLOps, pipeline development, and CI/CD (an asset);
- Knowledge of MLflow, DVC or equivalent model management solutions (an asset);
- Proficient in machine learning model monitoring tools (an asset);
- Experience with Infrastructure as Code approaches, including Terraform and Ansible (an asset);
- Experience in CI/CD applied to machine learning solutions or data pipelines (an asset);
- Knowledge of the R language (an asset);
- Demonstrate an interest in data architectures and cloud environment optimization (an asset);
- Certification in MLOps or related technology (an asset).

📌 Data Engineer (Montreal)
🏢 COFOMO
📍 Montreal