Data Engineer

Our GFL team is expanding, and we are seeking a highly skilled Data Engineer with experience in designing and maintaining real-time data streaming pipelines and building robust data lake infrastructure and architecture. The right candidate will be excited by the prospect of optimizing and building data architecture to support our next generation of AI-powered products and data initiatives. You will be a core contributor to our real-time operational efficiency platform and financial forecasting initiatives, owning the full data lifecycle — from raw ingestion through to AI-driven insights and statistical modelling.

Why Join GFL?
GFL Environmental is a major diversified environmental services company in North America. Our employees, collectively known as 'Team Green,' enjoy numerous benefits: career advancement opportunities, competitive benefits, job stability within an essential services company, and the chance to make a positive impact every day for our customers and communities, Green for Life!

What’s in it for You:

• Own the full data stack — from pipeline architecture to AI-driven insights — in a team that does it all.

• Work at the intersection of large-scale data engineering, statistical modelling, and cutting-edge AI, building the infrastructure that powers operational efficiency and financial forecasting across the business.

• Be part of a startup-like environment where your ideas and contributions directly impact the company's success.

• Take full ownership of projects and have the autonomy to drive them from inception to completion.

• Grow your career in a fast-paced environment with plenty of opportunities to work with the latest AI and data technologies.

• Collaborate with a passionate, cross-disciplinary team that values your input and encourages professional growth.

A “Day in the Life" of a Data Engineer:

• Design, develop, and maintain scalable, real-time and batch data pipelines using AWS Glue, Lambda, Apache Spark, and Kafka to power operational efficiency and financial forecasting use cases.

• Implement and evolve data lake architecture to ensure efficient data storage, processing, and retrieval at scale.

• Build and own end-to-end AI and ML workflows — including feature engineering, model training infrastructure, and real-time inference pipelines — without reliance on a separate data science function.

• Apply statistical modelling techniques to validate data quality, develop financial forecasting models, and design analytically rigorous data products.

• Collaborate with cross-functional teams — including Finance, Operations, and Product — to understand data requirements and translate them into scalable, intelligent data solutions.

• Use Terraform/CloudFormation to manage and provision infrastructure in a reproducible and scalable manner.

• Optimize and troubleshoot complex data pipelines, ensuring high availability and performance across real-time operational efficiency workflows.

• Explore and integrate the latest AI/ML technologies to continuously enhance our data processing and intelligence capabilities.

• Work in a fast-paced, startup-like environment where you will take full ownership of key projects and drive them from inception to completion.

Knowledge, Skills and Abilities:

• Proficient in AWS services, particularly Glue and ECS.

• Strong experience with Apache Spark and Delta Lake for big data processing.

• Hands-on experience with Kafka and real-time data streaming pipelines.

• Expertise in using Terraform for Infrastructure as Code (IaaC).

• Working knowledge of statistical modelling concepts (e.g. regression, time-series forecasting, anomaly detection) and their application to financial and operational use cases.

• Ability to own AI/ML workflows end-to-end, including building pipelines that serve predictive models and real-time scoring systems.