Manager, Data Engineering

About Jobber

Jobber passionately supports small home service businesses such as plumbers, painters, and landscapers by providing technology that simplifies quoting, scheduling, invoicing, and payments—all crafted to enhance the customer's experience. In an era where service delivery and technology are evolving rapidly, Jobber empowers entrepreneurs to manage their businesses flexibly and effectively.

This leading company embraces a culture centered on transparency, inclusivity, collaboration, and continuous innovation, earning accolades such as Great Place to Work and inclusion in Canada’s Top Growing Companies and Deloitte’s Technology Fast 50. With a seasoned executive team directing it, Jobber continues to expand its impact since launching its first customer in 2011.

Jobber fosters professional development through extensive onboarding, tutorials, hackathons, and mentoring programs, supporting all levels of experience. Leadership encourages empathy and actively promotes healthy work-life balance. Join Jobber to pursue your professional aspirations with dedication and passion.

The Team

The Data Integration team at Jobber is dedicated to equipping various departments with timely, accurate data to accelerate business value delivery. The team's responsibilities encompass data ingestion, activation, platform administration, self-serve tools, and ensuring data integrity and governance.

Role Overview

Reporting to the Director of Data, the Manager of Data Engineering will lead a team of data engineers, collaborating closely with a Technical Program Manager on prioritization and roadmap creation, spanning data and machine learning platform initiatives.

Key accountabilities include managing team performance and growth, ensuring high-quality deliverables, scaling the team, driving technical decisions in partnership with senior engineering leaders, and fostering a culture of collaboration and continuous learning.

Key Responsibilities

• Facilitate team members in honing their skills while collaborating on exceptional experiences and systems.
• Support individual success through goal setting, regular one-on-ones, constructive feedback, and mentorship.
• Recruit top talent and develop internal capabilities, implementing processes that enhance delivery and teamwork.
• Own the strategy, roadmap, and execution of scalable, high-performance, and cost-effective data infrastructure including data storage, computation, and orchestration.
• Guarantee system resilience, observability, and governance by applying recovery strategies, monitoring, and security best practices.
• Collaborate cross-functionally with engineering, analytics, and go-to-market teams to deliver structured, high-quality product data and build automations and tools that optimize workflows impacting small business customers.
• Innovate and improve data tools by leveraging AI technologies to continually enhance the team's capabilities.

Required Qualifications

• Demonstrable experience managing engineering teams, preferably within data engineering, with a record of delivering superior software and data solutions.
• Strong foundational knowledge in software and data engineering, including expertise in distributed data systems, cloud infrastructure, orchestration, performance tuning, scaling, and cost management.
• Hands-on skills in system design, SQL, contemporary data tools, and adherence to data best practices concerning modeling, governance, and quality assurance.
• Experience implementing observability systems, SLAs, disaster recovery, and compliance measures to ensure dependable data environments.
• Capability to lead agile teams fostering continuous learning, critical reasoning, and creative problem-solving.
• Excellent cross-team communication and collaboration skills, with proficiency in mentoring and coaching direct reports.
• Strategic vision and experience in planning roadmaps for infrastructure projects with significant business impact.
• Strong leadership qualities and mentorship skills to promote career advancement and help the team exceed targets.

Preferred but Not Mandatory

• Practical experience with modern data stack technologies such as Redshift, Trino, dbt, Airflow, Kafka, and familiar with Spark and Ray frameworks.
• Background developing internal developer platforms, self-service data tools, or workflow automation aimed at data teams.
• Knowledge of lambda or kappa data architectures, including batch and streaming processing, with real-world implementation experience.
• Collaboration experience influencing upstream data design and instrumentation within engineering teams.
• Exposure to infrastructure needs related to data science and machine learning workflows.

Compensation & Benefits

The role offers a transparent compensation range with a base salary starting at $169,200 CAD, a typical midpoint of $199,100 CAD, and a maximum of $228,900 CAD, reflecting the progression from onboarding to expert performance.

This package includes equity incentives, annual wellness stipends, retirement savings matching, and comprehensive health coverage with full premium payments for physical and mental health. Employees gain access to career coaching and development programs supported by a dedicated Talent Development team.

Work Environment & Culture

• Enjoy a comprehensive benefits package including extended health insurance, RRSP/TFSA/FHSA matching, and stock options.
• Participate in professional growth initiatives and leadership opportunities to help reach your full potential.
• Contribute to transforming a $400-billion industry without a dominant provider yet, making a real impact.
• Collaborate with a supportive, humble team passionate about customer success.

Diversity & Inclusion

Jobber values diversity and fosters an inclusive workplace. The company welcomes applicants from varied backgrounds and is an equal opportunity employer committed to accommodating candidates during the hiring process.