Data manager Jobs in Toronto, ON

Description

Key Responsibilities

• Lead, mentor, and grow a team of Data Engineers, including recruitment, onboarding, performance management, and continuous skill development.
• Oversee the design, development, and optimization of data pipelines, architectures, and data sets for batch and streaming use cases.
• Collaborate with business units and analysts to gather requirements and translate them into scalable data solutions.
• Drive best practices in data modeling (conceptual, logical, and physical) to support analytics and reporting.
• Establish and maintain metadata standards, data cataloging, and lineage to improve data discovery and governance.
• Ensure timely delivery of projects by managing priorities, scope, and resources in an Agile framework.
• Partner with vendors and managed service providers as required to support delivery and operations.
• Define, track, and report on team KPIs such as data pipeline uptime, data freshness, and adoption metrics.
• Contribute to capacity planning, cloud cost optimization, and vendor performance review.
• Promote standardization and reusability of data models, pipelines, and integration approaches.
• Serve as a subject matter expert, communicating technical solutions to both technical and non-technical audiences.
• Maintain compliance with organizational standards for security, architecture, and development practices.
• Encourage experimentation with automation, AI/ML integration, and modern data engineering practices.
• Provide input into enterprise architecture and align data initiatives.

Skills, Knowledge & Expertise

• Bachelor’s degree in computer science, Engineering, or related technical discipline.
• 5+ years of experience in data engineering, including 2–3 years in a leadership or managerial role.
• Proficiency in Python, SQL, and Spark (batch and streaming).
• Experience developing and maintaining data lakes and cloud-based platforms (preferably Azure/Databricks).
• Strong background in data architecture, including data modeling (conceptual, logical, dimensional) and relational databases.
• Familiarity with data mesh or federated architectures for distributed data ownership.
• Demonstrated ability to quantify business impact of data initiatives (e.g., cost savings, reduced downtime, increased adoption).
• Knowledge of data governance, data quality, and master data management practices.
• Proven ability to mentor and develop technical talent.
• Hands-on experience with Agile/Scrum methodologies.
• Excellent communication, problem-solving, and collaboration skills with a strong delivery-focused mindset.
• Knowledge of orchestration tools (SSIS, ADF, DBT or equivalent).
• Familiarity with DevOps/DataOps practices—CI/CD, version control, automated testing for data pipelines.
• Exposure to streaming technologies like Kafka/Event Hubs beyond Spark Streaming.
• Understanding of security and compliance frameworks (PII, GDPR, SOC2, HIPAA depending on industry).