Data Engineer

CoFoMo Inc.; Montreal (administrative region); On-site; CAD 90,000 - 140,000; Full time

Job summary

CoFoMo Inc. is seeking an experienced data engineer to design, develop, and optimize ETL/ELT pipelines for structured and unstructured data across cloud platforms. You will integrate sources, build data models, and ensure scalable data flows for analytics and AI initiatives.

You will collaborate with data scientists and business teams, implement quality controls, and document best practices. Proficiency with Python, Docker, Terraform, and CI/CD is valued, along with a strong foundation in data

Qualifications

• Bachelor's degree in software engineering or computer science required.
• Minimum six years of experience in data engineering, MLOps, software development, or ML.
• Strong Python proficiency and containerization experience (Docker).
• Experience with AWS or Azure; GCP is a plus.
• Knowledge of ML concepts and mathematical foundations is useful.
• Solid Linux and CI/CD experience; Terraform/Ansible is a plus.
• Experience with data pipelines, ETL/ELT, and model management tools is an asset.

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 data models for analytics, AI, and reporting needs.

Skills

Python

ETL/ELT

Data pipelines

AWS

Azure

GCP

Linux

Docker

Terraform

Ansible

CI/CD

MLOps

Data modeling

Data security

Education

Bachelor's degree in software engineering

Master's degree in computer science

Tools

Docker

Terraform

Ansible

CI/CD tooling

MLflow / DVC (asset)

Cloud platforms (AWS/Azure/GCP)

Job description

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).