MLOps / Data Engineer
New
J
JobgetherAdTech data
Candidates must be based in Ontario or Quebec, Canada.Full-Time
Salary100,000 - 125,000 CAD per year
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Job Details
- Languages
- Advanced English proficiency is required. French proficiency is considered an asset.
- Required Skills
- AWSPythonGCPKubernetesMicrosoft AzureSparkCI/CDDatabricks
Requirements
- Have proven experience in Data Engineering, MLOps, or DevOps roles involving automation, distributed systems, and scalable infrastructure.
- Bring strong Python programming skills and hands-on experience with Apache Spark.
- Have advanced Databricks experience, including Delta Lake, structured streaming, and feature engineering.
- Understand continuous integration and continuous delivery principles and have practical experience with CI/CD tools such as GitHub Actions, Jenkins, Azure DevOps, GitLab CI, or ArgoCD.
- Be familiar with AWS, Microsoft Azure, or Google Cloud Platform for data engineering and machine learning workloads.
- Have experience with Kubernetes or OpenShift, including deploying and scaling data and ML workloads and ensuring fault tolerance.
- Demonstrate technical judgment in evaluating infrastructure cost, performance, scalability, and maintainability trade-offs.
- Have advanced English proficiency; French proficiency is an asset.
- Be based in Ontario or Quebec, Canada.
- Knowledge of Scala is a significant advantage.
- Familiarity with IAB data standards, programmatic advertising infrastructure, or AdTech data pipelines is a strong advantage.
Responsibilities
- Build and manage automated CI/CD pipelines for machine learning workflows using tools such as GitHub Actions, Azure DevOps, Jenkins, GitLab CI, or ArgoCD.
- Develop and optimize distributed data pipelines using Apache Spark, PySpark, and Scala to process large-scale listener datasets.
- Configure and maintain Databricks infrastructure, optimize cluster performance, manage job scheduling, and control operational costs.
- Collaborate with data scientists to deploy machine learning models into production through scalable APIs and batch-processing systems.
- Implement automated testing, monitoring, and alerting to maintain pipeline reliability, reproducibility, and performance.
- Promote version control, model registry management, environment reproducibility, and automation standards.
- Evolve listener data systems toward structured, continuously available, machine-readable data feeds for advertising platforms.
- Evaluate architectural decisions and optimize infrastructure for performance, scalability, cost efficiency, and maintainability.
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