Data Engineer, Investments

iA Financial Group•Montreal, QC

22h•Hybrid

About The Position

As a Data Engineer, you will play a key role in the design, industrialization, and optimization of modern data pipelines on the Google Cloud platform. You will actively contribute to building a robust, scalable, and AI-driven data architecture. You will work closely with multidisciplinary teams to deliver concrete, high value-added solutions for the organization. This position represents a great opportunity to showcase your skills and fully leverage your potential within a caring and reliable company. Here, people and their development are at the heart of our priorities, fostering an environment conducive to collaboration and innovation.

Requirements

5 to 10 years of experience in data engineering, ETL/ELT development, or a related field.
Degree in computer science, software engineering, or an equivalent field.
Mastery of data modeling concepts (Data Vault, star schema, etc.).
Strong experience with BigQuery and cloud analytics environments.
Strong experience with distributed processing (Spark / Dataproc).
Expertise in pipeline orchestration (e.g., Prefect, Airflow, or equivalent).
Comfortable with modern development practices: GitHub, GitHub Actions CI/CD, Infrastructure as Code (Terraform).
Familiarity with microservices-oriented architectures (Cloud Run or equivalent).
Understanding of data governance, security, and cloud access management (IAM).
Strong analytical skills and the ability to propose relevant solutions.
Autonomy, rigor, and a constant focus on quality.
Ability to thrive in an agile and multidisciplinary environment.
Excellent proficiency in French, both spoken and written.
Intermediate level of English, as the role involves leading presentations and meetings with internal and external stakeholders on a weekly basis.

Nice To Haves

Experience in a production GCP environment
Knowledge of MLOps/DataOps practices
Knowledge of investment and financial data (portfolios, transactions, market prices)
GCP certification (e.g., Professional Data Engineer)

Responsibilities

Design, develop, and maintain high-performance, secure, and scalable data pipelines (ETL/ELT) on Google Cloud Platform (GCP).
Leverage GCP services such as: BigQuery (analytics data warehouse), Cloud Storage (raw data and data zones storage), Dataproc (distributed Spark/Hadoop processing), Cloud Run (deployment of data services and microservices), Cloud Workstations (development environments).
Participate in the implementation of modern data lakehouse architectures.
Design and orchestrate data processing pipelines using Prefect.
Implement robust, observable, and resilient workflows.
Develop in modern environments (VS Code / Cloud Workstations).
Manage source code using GitHub.
Implement CI/CD pipelines with GitHub Actions.
Automate deployments and infrastructure using Terraform (Infrastructure as Code).
Prepare, transform, and structure data to support analytics and artificial intelligence use cases.
Collaborate with data scientists to facilitate access to reliable and governed data.
Implement DevOps/DataOps practices: Automated testing, Pipeline monitoring, Error handling and alerting.
Ensure data quality, traceability, and governance.
Work collaboratively with: Product Owners and Product Managers (PO & PM), Architects, Data Scientists, Business partners.
Translate business needs into robust technical solutions.
Document solutions and contribute to their continuous evolution.