MLOps / Data Engineer

Job summary

The MLOps Data Engineer bridges data science and production systems, building reliable machine-learning workflows that turn listener intelligence into structured, queryable signals for advertising. The role develops CI/CD pipelines, optimizes large-scale listener data processing with Apache Spark and Databricks, productionizes models, and implements pipeline testing and monitoring for audio inventory across podcast, streaming, and broadcast radio.

Benefits

Qualifications

• Experience in data engineering, MLOps, or DevOps focused on automation and scalability
• Strong Python skills and hands-on Apache Spark experience
• Advanced Databricks expertise, including Delta Lake, structured streaming, and feature engineering
• Understanding of CI/CD principles and tools
• Familiarity with AWS, Azure, or GCP for data and machine-learning workloads
• Experience with Kubernetes or OpenShift deployment, scaling, and fault tolerance
• Ability to assess cost, performance, scalability, and maintainability trade-offs
• Strong problem-solving skills and ability to work with cross-functional teams
• Advanced English; French is an asset
• Familiarity with IAB data standards, programmatic advertising infrastructure, or AdTech data pipelines is an asset
• Must be based in Ontario or Quebec

Responsibilities

• Design, implement, and maintain CI/CD pipelines for machine-learning workflows
• Build and optimize Spark pipelines for large-scale distributed listener datasets
• Deploy and manage Databricks environments, clusters, and scheduled jobs while optimizing costs
• Collaborate with data scientists to productionize models in scalable APIs or batch systems
• Implement automated testing, monitoring, and alerting for ML pipelines
• Maintain best practices for version control, model registries, and reproducible environments
• Develop structured, queryable listener data feeds for automated advertising systems

Skills

Languages

Industry

Relocation