Manager Data Engineering-ML Ops

Role overview

Responsibilities

• Design, build, and maintain pipelines for ingestion, transformation, and storage of large-scale datasets.
• Optimize data workflows for processing efficiency and availability.
• Set practices for deploying, monitoring, and managing production pipelines and ML models.
• Partner with data scientists to integrate and deploy ML solutions.
• Set the data engineering and MLOps vision, strategy, and roadmap aligned with company goals.
• Ensure pipelines and models are scalable, reliable, and efficient.
• Lead and coach the engineering team; foster innovation and best practices.
• Allocate resources, manage workloads, and set goals for timely, budget-compliant delivery.
• Ensure compliance with standards, regulations, and security practices; protect sensitive data.
• Monitor technology developments and drive continuous improvement.

Qualifications

• Bachelor’s or master’s degree in computer science, data engineering, software engineering, or a related field.
• 5+ years in data engineering, MLOps, or a related field, including at least 2 years in leadership or management.
• Advanced proficiency in Python, Java, or Scala; knowledge of data pipeline frameworks such as Databricks and MLOps tools such as MLflow or Kubeflow.
• Hands-on experience with AWS, Google Cloud, or Azure, and Docker and Kubernetes.
• Expertise in data architecture, data modeling, and database systems; strong problem-solving and analytical skills.
• Strong leadership, team management, communication, adaptability, prioritization, and project-management capabilities.

Compensation

• Expected base salary range is CAD 118,500–168,500; compensation may vary based on experience, skills, qualifications, market conditions, and business needs.
• Eligible employees may receive discretionary bonus compensation subject to company and individual performance.
• Benefits include health and dental coverage, disability insurance, retirement plan matching, paid time off, and recognition programs.