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Jobber is a SaaS platform helping small home service businesses (plumbers, painters, landscapers) manage quoting, scheduling, invoicing, and payments. The company has grown from its first customer in 2011 and is recognized as a Great Place to Work and listed on Canada's Top Growing Companies and Deloitte Technology Fast 50.
The Data Integration team's mission is to empower Jobber teams with the right data at the right time in the right place. The team owns data ingestion, data activation, platform administration, self-serve tooling, and data governance.
As Manager, Data Engineering, you will lead a team of data engineers reporting to the Director of Data. You'll partner with a Technical Program Manager on quarterly roadmaps and own strategy, roadmap, and delivery of scalable, cost-efficient data infrastructure including data stores, compute engines, and orchestration systems.
Key responsibilities include: managing team performance and individual growth; recruiting and scaling a world-class team; owning data infrastructure strategy and delivery; ensuring resilient, observable, and governed data systems with robust recovery and monitoring; partnering across engineering, analytics, and go-to-market teams to deliver high-quality product data and automation; and driving innovation through AI tools to improve data tooling.
You should have proven experience managing engineering teams (ideally data engineering), strong technical foundation in distributed data systems, orchestration frameworks, cloud infrastructure, and cost optimization. Hands-on expertise in systems design, SQL, modern data tools, data modeling, governance, and quality management is essential. Experience with observability, SLAs, and disaster recovery is required. You'll need excellent collaboration and communication skills, strategic thinking for roadmap planning, and strong mentorship capabilities.
Highly desired: hands-on experience with modern data stack tools (Redshift, Trino, dbt, Airflow, Kafka), data processing frameworks (Spark, Ray), building internal developer platforms or self-service data tooling, and understanding of lambda/kappa architectures.
