SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: CAD 169,200 - 228,900 / annual
Jobber is a B2B SaaS platform helping small home service businesses (plumbers, painters, landscapers) manage quoting, scheduling, invoicing, and payments. The company has been recognized by Great Place to Work and named to Deloitte Canada's Technology Fast 50 and Globe and Mail's Canada's Top Growing Companies lists.
The Data Integration team's mission is to empower teams across Jobber with the right data, at the right time, in the right place. The team manages data ingestion and Change Data Capture (CDC), materializing CDC streams into Snowflake tables, data activation (egress), data movement and transformation systems, self-serve tooling, and data integrity and governance.
As a Staff Data Engineer, you will be a technical champion and force multiplier, leading and mentoring a team of data engineers while solving complex technical challenges. Your role combines deep technical expertise with strategic leadership. You will shape foundational data components by designing, building, and maintaining scalable batch and real-time data pipelines. You'll demonstrate technical mastery by delivering high-quality solutions, championing best practices, and mentoring team members. You will drive reliability and resilience by establishing testing standards, SLAs, and disaster recovery playbooks. You'll advance observability and governance by building monitoring, logging, lineage, and auditing frameworks. You'll accelerate data access by developing self-serve tools and automation that reduce manual effort and enable teams across engineering, analytics, and data science to work effectively with data.
You will contribute to strategic planning by partnering with Technical Program Managers on roadmaps, drive cross-team collaboration with other Staff Engineers and technical leaders, and work closely with data analysts, scientists, and product teams to enable fast exploration, analysis, modeling, and reporting. You'll own data integrity and reliability, ensuring stakeholders maintain trust in data-driven insights and decisions. The role includes on-call rotation participation (one week at a time), with time off in lieu provided for incidents outside regular working hours.
Jobber offers a total compensation package including extended health benefits with fully paid premiums, RRSP/TFSA/FHSA matching, stock options, and access to a dedicated Talent Development team with career coaching and leadership programs.
REQUIREMENTS:
Core competencies required:
- Hands-on experience with batch and real-time data processing frameworks, lakehouse/warehouse management, large-scale data transformation, data serialization, workflow orchestration, and dimensional modeling (star/snowflake schemas)
- Proven ability to design and deliver highly scalable, maintainable, and high-performance solutions across multiple technology stack layers, leveraging containerization, CI/CD, and API development
- Strong understanding of AWS services relevant to the data domain, with hands-on experience designing and implementing data solutions
- Demonstrated success guiding teams through complex, high-impact projects while providing architectural direction and serving as a trusted technical lead
- Exceptional proficiency in software design, system architecture, and coding, with focus on long-term maintainability, performance, and resilience
- Strong background in infrastructure-as-code (Terraform, CloudFormation), observability (logging, monitoring, tracing), and system reliability
- Exceptional communication skills, self-motivation, and resourcefulness, with ability to navigate ambiguity, prioritize effectively, and deliver results in fast-paced environments
Nice-to-have experience:
- Machine Learning platform experience and exposure to distributed compute frameworks (Ray, TensorFlow, PyTorch)
- Cross-domain engineering experience with non-data engineering codebases (Ruby on Rails, TypeScript/React)
- API and integration knowledge (GraphQL, API layer design, performance optimization)
- Platform building experience supporting multiple engineering domains
- Data privacy, security, and compliance knowledge in cloud-based data environments
