Manager, Data Engineering

About Jobber and the Role

At Jobber, we empower small home service businesses such as plumbers, painters, and landscapers by providing technology that helps them quote, schedule, invoice, and collect payments efficiently, delivering a professional customer experience. As the industry evolves rapidly, Jobber supports these businesses in adapting to modern customer expectations.

Recognized for our culture of transparency, inclusivity, collaboration, and innovation, Jobber is proud to be acknowledged by multiple prestigious awards and industry rankings.

We provide extensive support and resources for employee growth, including onboarding materials, tutorials, hackathons, mentoring, and leaders dedicated to fostering work-life harmony.

Team Environment

The Data Integration team focuses on delivering the right data at the right time and place to enable teams across Jobber to create business value efficiently. Their responsibilities include data ingestion, activation, platform management, self-serve tools, and ensuring data integrity and governance.

Position Summary

Reporting to the Director of Data, the Manager, Data Engineering, leads a team of data engineers collaborating closely with a Technical Program Manager to prioritize initiatives and develop quarterly roadmaps. This role covers both Data and Machine Learning platform domains.

Key Responsibilities

• Facilitate team performance by supporting mastery of their craft and encouraging the creation of exceptional systems and experiences.
• Drive individual success through goal-setting, regular one-on-ones, constructive feedback, and career mentorship.
• Expand and elevate the team by recruiting skilled talent, enhancing internal abilities, and establishing processes to improve delivery and collaboration.
• Own the strategy, roadmap, and execution of scalable, cost-effective, high-performance data infrastructure, including data storage, computation engines, and orchestration systems.
• Assure data platforms are resilient, observable, and governed, implementing recovery plans, monitoring, and maintaining security and compliance.
• Collaborate across engineering, analytics, and go-to-market teams to provide high-quality, well-structured product data and develop tools and automations that streamline workflows and benefit Jobber's customers.
• Promote innovation and efficiency by integrating AI technologies to enhance data strategy and support continuous experimentation and improvement of data tools.

Required Qualifications

• Demonstrated experience leading engineering teams, preferably focused on data engineering, with a proven track record in delivering high-quality software and data solutions.
• Strong technical expertise in software and data engineering, including distributed data systems, orchestration frameworks, cloud infrastructure, performance optimization, scaling approaches, and cost management.
• Proficiency in system design, SQL, modern data tooling, and best practices involving data modeling, governance, and quality assurance.
• Experience implementing observability tools, defining SLAs, disaster recovery procedures, and other measures ensuring reliable, resilient, and compliant data systems.
• Ability to lead effectively in agile settings, cultivating a culture of continuous learning, analytical thinking, and innovative problem solving.
• Excellent communication and collaboration skills with cross-functional teams, including product, analytics, and data science, while mentoring and coaching direct reports.
• Strategic mindset with experience in planning infrastructure roadmaps and initiatives that deliver measurable business impact.
• Strong leadership and mentoring capabilities to guide, motivate, and grow team members, ensuring their career development and team success.

Preferred but Not Mandatory Skills

• Practical knowledge of modern data stack tools such as Redshift, Trino, dbt, Airflow, Kafka, and data processing frameworks like Spark and Ray.
• Experience building internal developer platforms, self-service data tooling, or workflow automation for data teams.
• Understanding and implementation experience of lambda and/or kappa architectures, batch and streaming data processing in production environments.
• Experience collaborating with engineering teams to influence data design and instrumentation upstream.
• Exposure to data science and machine learning infrastructure and workflows.

Compensation and Benefits

Jobber maintains transparent and equitable compensation practices. This role offers a base salary range from $169,200 CAD to $228,900 CAD annually, reflecting starting proficiency to exceptional performance levels, with typical hires placed near the $199,100 midpoint.

In addition to salary, the total package includes equity rewards, annual wellness stipends, retirement savings matching, and comprehensive health coverage with fully paid premiums for physical and mental health.

Employees also gain access to dedicated talent development programs featuring career coaching and advancement opportunities.

Work Culture and Inclusion

• Enjoy a benefits package comprising extended health, RRSP, TFSA or FHSA matching, and stock options.
• Join a team focused on humility, support, and genuine care for customers.
• Engage in a workplace committed to diversity and inclusivity, welcoming various backgrounds and perspectives.

Jobber supports accommodations throughout the hiring process and strives to create an environment where all employees thrive.

About Jobber's Impact

By enabling small businesses like landscaping and cleaning services to better connect with customers, save administrative time, and expedite payments, Jobber actively contributes to a $400-billion industry seeking innovation and leadership.