ML/Data Engineer

GuruLink; Toronto; On-site; CAD 80,000 - 110,000; Full time

Job summary

A leading technology firm in Toronto is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines and analytics platforms. The ideal candidate has strong expertise in Python, SQL, and cloud environments, along with experience in machine learning workflows. Join a collaborative team committed to transforming engineering and design workflows across North America.

Qualifications

• 3+ years of experience in data engineering, ML engineering, or data science.
• Strong proficiency in Python and SQL.
• Experience with cloud data environments (AWS preferred).

Responsibilities

• Design and implement automated, scalable data pipelines.
• Modernize existing ETL workflows using Python and cloud-native tools.
• Build and maintain data models that support analytics.

Skills

Python

SQL

Data Engineering

Machine Learning

Cloud Data Environments

Data Pipelines

Git

CI/CD

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Pandas

PySpark

AWS

Airflow

TensorFlow

Job description

Overview

Location: Scarborough, Ontario

We are a leader in applying advanced technology to transform engineering and design workflows. Our team builds tools that enable engineers and architects to integrate complex systems with speed and precision for some of the most innovative building projects across North America. We pride ourselves on a strong work ethic, a commitment to exceptional client service, and a motivated, collaborative culture.

Data is at the core of our solutions. We are seeking professionals who can design, build, and maintain reliable data pipelines and analytics platforms that support our engineering and design products. In this role, you will partner closely with cross-functional teams to turn raw datasets into meaningful insights and ensure a scalable, resilient data ecosystem. We look for individuals who are resourceful, proactive, adaptable, and team-oriented, with excellent problem-solving capabilities.

Responsibilities

• Design and implement automated, scalable data pipelines for ingestion, transformation, and integration across varied customer systems.
• Modernize existing ETL workflows using Python, cloud-native tools, and automation powered by machine learning.
• Build and maintain data models and architectures that support analytics, reporting, and ML applications.
• Apply machine learning techniques for data cleaning, anomaly detection, and predictive modeling to reduce manual processes.
• Collaborate with Data Analysts, Product teams, and Engineering to deliver accurate, actionable insights.
• Implement and uphold data quality, governance, and monitoring frameworks, leveraging automation when possible.
• Create dashboards and visualizations to present complex data in clear, business-friendly formats.
• Contribute to AI/ML initiatives, including prototyping and deploying production-ready models.
• Ensure compliance with security, privacy, and regulatory requirements.
• Advocate for best practices in automation, version control, and DevOps for data workflows.

Must Have Skills

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or related field (or equivalent experience).
• 3+ years of experience in data engineering, ML engineering, or data science.
• Strong proficiency in Python and SQL; experience with libraries such as Pandas, PySpark, or Dask.
• Demonstrated experience working with cloud data environments (AWS preferred; knowledge of Glue, Lambda, S3, Redshift, or Snowflake is an advantage).
• Hands-on experience building and sustaining data pipelines using platforms such as Airflow, dbt, or comparable tools.
• Solid understanding of machine learning workflows, model lifecycle management, and deployment practices.
• Experience with Git, CI/CD, and Infrastructure as Code.
• Excellent problem-solving and communication skills, with a collaborative approach to teamwork.

Nice to Have Skills

• Experience with LLM-driven data workflows, natural-language querying, or generative AI integrations.
• Knowledge of NoSQL technologies (MongoDB, DynamoDB) and real-time streaming systems (Kafka, Kinesis).
• Familiarity with visualization platforms such as Power BI, Tableau, or QuickSight.
• Experience with containerization and orchestration tools (Docker, Kubernetes).
• Experience deploying ML models using TensorFlow, PyTorch, or Scikit-learn.
• Understanding of data warehouse architecture and optimization (Snowflake, BigQuery, Redshift).
• Background working with construction or engineering-related data (AutoCAD, Revit APIs) is a significant plus.