MLOps / Data Engineer
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Key details
- Location
- Canada
- Work style
- Remote
- Job type
- Full-time
- Function
- Data & ML
- Focus area
- Data engineering & Analytics
- Posted
- 2h ago
Skills
About the role
This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a MLOps / Data Engineer based in Canada.
As an MLOps / Data Engineer, you will play a key role in bridging data science and production engineering, transforming machine learning models into reliable, scalable, and high-performance solutions. You will contribute to the development of advanced data infrastructure supporting the global digital audio advertising ecosystem. Working with large-scale listener datasets, you will design automated pipelines, optimize distributed data processing, and productionize machine learning workflows. Your work will help turn audience intelligence into structured, actionable signals for modern advertising platforms and emerging AI-driven buying systems. You will collaborate closely with data scientists and engineering teams in a technically advanced, cross-functional environment. This fully remote opportunity offers the chance to shape the future of data-driven advertising technologies.
Accountabilities:
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Design and maintain MLOps pipelines: Build, implement, and manage automated CI/CD pipelines for machine learning workflows using tools such as GitHub Actions, Azure DevOps, Jenkins, GitLab CI, or ArgoCD.
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Develop scalable data processing solutions: Build and optimize distributed data pipelines using Apache Spark, PySpark, and Scala to process large-scale listener datasets efficiently.
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Manage Databricks environments: Configure and maintain Databricks infrastructure, optimize cluster performance, manage job scheduling, and control operational costs.
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Productionize machine learning models: Collaborate with data scientists to deploy ML models into production through scalable APIs and batch-processing systems that generate actionable audience intelligence.
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Ensure reliability and performance: Implement automated testing, monitoring, and alerting mechanisms to maintain the reliability, reproducibility, and performance of data and machine learning pipelines.
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Establish engineering best practices: Promote effective version control, model registry management, environment reproducibility, and automation standards.
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Support next-generation advertising infrastructure: Help evolve listener data systems toward structured, continuously available, machine-readable data feeds that can be accessed and used by autonomous advertising platforms.
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Drive technical improvements: Evaluate architectural decisions and optimize infrastructure across performance, scalability, cost efficiency, and long-term maintainability.
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Professional experience: Proven experience in Data Engineering, MLOps, or DevOps roles, particularly involving automation, distributed systems, and scalable infrastructure.
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Programming expertise: Strong Python programming skills and hands-on experience with Apache Spark. Knowledge of Scala is a significant advantage.
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Databricks proficiency: Advanced experience with Databricks, including Delta Lake, structured streaming, and feature engineering.
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CI/CD knowledge: Strong understanding of continuous integration and continuous delivery principles, with practical experience using tools such as GitHub Actions, Jenkins, Azure DevOps, GitLab CI, or ArgoCD.
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Cloud expertise: Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform for data engineering and machine learning workloads.
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Containerization and orchestration: Experience working with Kubernetes or OpenShift environments, including deploying, scaling, and ensuring fault tolerance for data and ML workloads.
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Architectural capabilities: Strong technical judgment and the ability to assess trade-offs involving infrastructure costs, performance, scalability, and maintainability.
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Analytical and