Data Engineer

iA Financial Group•Montreal, QC

3d•Hybrid

About The Position

As a Data Engineer, you will play a key role in the design, industrialization, and optimization of data pipelines and artificial intelligence solutions. You will work closely with multidisciplinary teams to deliver high-value, impactful solutions to the organization. As a Data Engineer, you will be at the heart of our mission.

Requirements

A minimum of 7 years of relevant experience in data engineering, ETL development, or related fields.
Holds a university degree in computer science or a related discipline.
Demonstrates strong expertise in designing and orchestrating ETL/ELT pipelines using tools such as Azure Data Factory, with excellent SQL skills, knowledge of SSIS, and a solid understanding of data modeling (Data Vault, star schema, etc.).
Has strong experience with cloud databases, particularly Snowflake, and managing Azure platforms.
Acts as a technical leader with solid experience in Databricks (model management modules, Mosaic AI, Unity Catalog).
Is familiar with DevOps and MLOps practices, including CI/CD pipelines, solution and model monitoring, and thrives in agile, multidisciplinary environments (Scrum/Lean).
Possesses excellent communication skills (both written and verbal), strong initiative, rigor, and a constant focus on quality.
Is fluent in French and has an intermediate level of English.

Nice To Haves

Experience in the insurance or financial services industry is considered an asset.

Responsibilities

Designing, developing, and maintaining high-performance, secure, and scalable data pipelines (ETL/ELT).
Contributing to the implementation and evolution of data management solutions (data warehouses, data marts, data lakes).
Preparing, transforming, and structuring data required for training supervised machine learning models.
Developing, testing, deploying, and industrializing machine learning and AI models in cloud environments (Azure, Snowflake, Databricks).
Implementing and maintaining DevOps and MLOps practices, including continuous integration, pipeline monitoring, and model reproducibility.
Collaborating with Product Owners, Product Managers, data scientists, architects, and business stakeholders to translate business needs into concrete technical solutions.
Documenting delivered solutions and contributing to their sustainability and continuous improvement.