Senior Data Engineer - Machine Learning & Data Platforms - REMOTE

Senior Data Engineer- Machine Learning & Data Platforms | Remote

Our client is building the next generation of industrial intelligence, transforming complex automotive and industrial data into real-time, actionable insights powered by machine learning. This is a full-time remote position. You can work from Toronto, Ottawa or Montreal.

We are seeking an experienced Data Engineer who thrives in high-scale, production ML environments and enjoys working with complex, messy datasets to build reliable, scalable data foundations for advanced analytics.

You will join a team of 13 engineers and data professionals, working in a highly collaborative, fast-moving environment. The role is remote-friendly, with strong cross-functional interaction across engineering, data science, and business teams.

What You’ll Do

• Design, build, and maintain scalable data pipelines and ETL processes for large structured and unstructured datasets
• Develop and optimize Spark-based data workflows supporting production ML systems
• Collaborate closely with data scientists, ML engineers, and business stakeholders
• Translate complex business needs into scalable, production-grade data solutions
• Build feature engineering pipelines for time-series and predictive models
• Ensure data quality, governance, security, and reliability across systems
• Continuously improve data architecture, performance, and scalability

What You Bring

• Min. 6+ years in data engineering or ML data pipeline development
• Strong experience with Apache Spark, PySpark, Databricks, Delta Lake
• Advanced skills in Python, SQL, and Airflow
• Deep understanding of Medallion architecture and ETL design patterns
• Experience building time-series features (rolling windows, lags, trend indicators)
• Ability to work closely with ML teams and translate data into model-ready structures
• Bachelor’s or Master’s in Computer Science, Engineering, or related field

Preferred Experience

• Background in retail, e-commerce, or supply chain environments dealing with large, messy, high-volume datasets
• Experience working directly with machine learning teams or supporting ML model development
• Hands-on experience in forecasting, demand planning, or similar data-heavy business domains
• Experience integrating data from ERP/CRM/WMS systems (SAP, Oracle, legacy platforms)
• Exposure to feature stores or ML training/serving consistency frameworks
• Experience with IBM DataStage or legacy ETL modernization projects
• Experience scaling distributed ML or time-series models in production environments

Why This Role

This is a strong fit for someone who enjoys working in complex, real-world data environments, especially with messy, high-volume retail-style data and close collaboration with ML teams. Retail or similar domains are highly valued.