Data Engineer

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 environment;

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).

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