J
Jobber
Manager of Data Engineering
Skills
SQL QueriesData EngineeringData ScienceKafkaData AnalysisMachine LearningAIdbtNo-code AutomationsApache AirflowCompliance
1 month ago
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Important information
Contract type:
Permanent
Location:
Canada
Work mode:
Hybrid
Published on:
16 September 2026
What they need
- Reporting to the Director of Data, the Manager, Data Engineering will lead a team of data engineers
- You will also partner with the team’s Technical Program Manager to prioritize initiatives and collaborate on building quarterly roadmaps
- This role’s scope is large as it spans both Data and ML platforms
- A key aspect of this role is managing the team’s performance, supporting individual growth, ensuring high-quality deliverables, and scaling the team as needed
- You will also play a key role in shaping technical decisions, collaborating with technical leads, principals, and distinguished engineers
- Live and breathe performance facilitation by helping your team master their craft while collaborating to build extraordinary experiences and systems
- Be committed to your people’s success by setting goals, holding regular 1-on-1s, providing constructive feedback, and mentoring team members to grow their careers
- Develop and scale a world-class team by recruiting top talent, leveling up internal capabilities, and implementing processes that improve delivery and collaboration
- Own the strategy, roadmap, and delivery of high-performance, scalable, and cost-efficient data infrastructure including data stores, compute engines, and orchestration systems
- Ensure data systems are resilient, observable, and governed. Implementing robust recovery strategies, proactive monitoring, and best practices for security, integrity, and compliance
- Partner across engineering, analytics, and go-to-market teams to deliver well-structured, high-quality product data and build tools, automation, and solutions that accelerate workflows and create impactful outcomes for Jobber’s small business customers
- Drive innovation and efficiency with the use of AI tools to support the data strategy. Enable team to continuously explore, experiment and improve the state of Data tools with the help of AI- Excellent collaboration and communication skills, with the ability to work cross-functionally with engineering, product, analytics, and data science teams while mentoring and coaching direct reports
- The ability to lead and adapt in an agile environment, fostering a culture of continuous learning, critical thinking, and creative problem-solving
- Strategic thinking and roadmap planning capabilities, with experience shaping infrastructure initiatives that have measurable impact
- Experience implementing observability frameworks, SLAs, disaster recovery strategies, and other practices to ensure resilient, reliable, and compliant data systems
- A strong technical foundation in software and data engineering, including distributed data systems, orchestration frameworks, cloud infrastructure, performance tuning, scaling strategies, and cost optimization
- Hands-on experience in systems design, SQL, modern data tools, and data best practices, including modeling, governance, and quality management
- Strong leadership and mentorship skills, using your experience to guide, influence, and provide constructive feedback to direct reports—ensuring their growth and enabling the team to exceed its goals
- Proven experience managing engineering teams - ideally in data engineering domains- with a track record of delivering high-quality software and data solutions
- Hands-on experience with modern data stack tools such as Redshift, Trino, dbt, Airflow, Kafka, and familiarity with data processing frameworks such as Spark and Ray
- Background in building internal developer platforms, self-service data tooling, or workflow automation for data teams
- Sound understanding of lambda and/or kappa architecture, batch and streaming principles and experience implementing either of the two architectures in a production environment
- Experience in working with Engineering teams to influence upstream data design and instrumentation
- Exposure to data science and machine learning workflows and their infrastructure requirements
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