Senior Data Engineer - Machine Learning & Data Platforms - REMOTE

Talent To Hire Inc.; Toronto; Remote; CAD 100,000 - 130,000; Full time

Job summary

TEEMA Solutions Group is looking for a Senior Data Engineer to develop scalable data pipelines and optimize Spark workflows. This remote role requires a minimum of 6 years in data engineering or ML data pipeline development, with a strong foundation in Apache Spark, Python, and SQL. Ideal candidates will have experience with complex datasets, data governance, and close collaboration with machine learning teams. Enjoy working in a dynamic environment focused on actionable insights from data.

Qualifications

• 6+ years in data engineering or ML data pipeline development.
• Experience with complex, messy datasets for production ML systems.
• Understanding of Medallion architecture and ETL design patterns.

Responsibilities

• Design, build, and maintain scalable data pipelines for large datasets.
• Develop and optimize Spark-based workflows for production ML systems.
• Collaborate with data scientists and business stakeholders.

Skills

Apache Spark

PySpark

Databricks

Python

SQL

Airflow

Education

Bachelor’s or Master’s in Computer Science, Engineering, or related field

Job description

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.