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.
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