Tasks and responsibilitiesDesign, 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.QualificationsBachelor'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 withGoogle 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 withMLOps tools and practices (an asset); Understanding the principles of computer networking (an asset); Have experiencein 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 withInfrastructure 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