Senior Software Engineer–Data & ML Platform

Jobber

1d

About The Position

Does working with Data motivate and excite you?
Then Jobber might be the place for you! We’re looking for a Senior Software Engineer to be part of our Data & ML Platform team.

Jobber exists to help people in small businesses be successful. We work with small home service businesses, like your local plumbers, painters, and landscapers, to transform the way service is delivered through technology. With Jobber they can quote, schedule, invoice, and collect payments from their customers, while providing an easy and professional customer experience. Running a small business today isn’t like it used to be—the way we consume and deliver service is changing rapidly, technology is evolving, and customers expect more. That’s why we put the power and flexibility in their hands to run their businesses how, where, and when they want!
Our culture of transparency, inclusivity, collaboration, and innovation has been recognized by Great Place to Work, Canada’s Most Admired Corporate Cultures, and more. Jobber has also been named on the Globe and Mail’s Canada’s Top Growing Companies list, and Deloitte Canada’s Technology Fast 50™, Enterprise Fast 15, and Technology Fast 500™ lists. With an Executive team that has over thirty years of industry experience of leading the way, we’ve come a long way from our first customer in 2011—but we’ve just scratched the surface of what we want to accomplish for our customers.
We help employees grow professionally; we have a ton of onboarding resources, tutorials, hackathons and buddies to support learnings and provide opportunities to innovate. We have a range of experience levels on teams which allows for mentor/mentee opportunities. Leaders at Jobber work with empathy and support employees to build healthy work-life harmony. Bring your dedication and passion to this job to fulfill your goals
The Team:
The Data & ML Platform team builds and operates the core data foundation that powers analytics and machine learning across Jobber. This highly technical team manages the lakehouse, CDC pipelines, and job orchestration to deliver accurate, timely data for downstream use. They establish standards for data modeling, observability, and security, while continuously optimizing the data ecosystem for performance and efficiency. By enabling analysts, data scientists, and engineers to self-serve through shared tools and capabilities, the team allows partners to focus on foundational transformations and building consistent data models. Ultimately, the goal is to provide robust, well-orchestrated systems that enable teams to move quickly and confidently as the business scales.

The role:
The Software Engineer, Data & ML Platform role sits at the intersection of software engineering, platform reliability, and data enablement, and is critical to how teams across Jobber build, ship, and scale. As a Senior Software Engineer on the Data & ML Platform team, you will take end-to-end ownership of data and machine learning platform capabilities—from design and implementation through operation and continuous improvement. You’ll apply strong backend and systems engineering skills to build durable, scalable platforms that enable data and machine learning use cases across the company.

Requirements

• Designing and building scalable, reliable distributed systems in a cloud environment (AWS or equivalent).
• Strong understanding of system design trade-offs, including scalability, fault tolerance, performance optimization, and cost efficiency.
• Strong proficiency in backend development using Python, with experience building production-grade services and APIs.
• Experience designing and maintaining APIs and internal services that support data workflows; Ability to write clean, maintainable, and well-tested code that supports long-lived platform capabilities.
• Solid experience working with SQL and large-scale data processing systems, including data warehouses and lakehouse-style platforms.
• Hands-on experience with data transformation and analytics tooling—not limited to dbt, Pandas, Polars, or similar frameworks used for data modeling, transformation, and analysis.
• Experience building and operating data pipelines, including CDC systems (e.g., AWS DMS or similar) and batch or streaming workflows.
• Familiarity with data quality practices such as schema enforcement, deduplication, and anomaly detection.
• Experience building and maintaining CI/CD pipelines to test, deploy, and operate backend, data, and platform systems.
• Familiarity with containerization and deployment workflows using tools such as Docker and cloud-native services.
• Strong operational mindset, including monitoring, alerting, incident response, and continuous improvement of developer workflows.
• Strong communication skills with the ability to work effectively across Product, Engineering, and ML teams.
• Experience partnering with stakeholders to translate requirements into well-designed technical solutions.
• Ability to document systems, share best practices, and contribute to a culture of operational excellence and continuous learning.

Nice To Haves

• experience designing or evolving data and platform systems such as lakehouse architectures, data warehouses, and orchestration platforms (e.g., Iceberg-based lakehouses, Redshift, Snowflake, Airflow, AWS Glue).
• familiarity with ML workflows and model deployment patterns, but deep ML expertise is not required.
• experience with streaming platforms (Kafka, Kinesis) or emerging data technologies such as vector databases.

Responsibilities

• Build scalable platforms, frameworks, and self-service tooling that enable engineers, analysts, and data scientists to work effectively with data and machine learning.
• Partner closely with Product, Engineering, and ML teams to understand their needs and translate them into intuitive, well-documented capabilities that improve developer experience and reduce friction.
• Design, operate, and evolve core platform systems—including compute platforms, job orchestration engines, CI/CD workflows, and production ML infrastructure—with a focus on performance, scalability, and cost efficiency.
• Continuously improve automation and operational practices to reduce toil and enable teams to move faster with confidence.
• Apply strong software engineering fundamentals to build new platform capabilities that unlock faster experimentation, safer deployments, and more scalable data and ML use cases.
• Contribute to long-term technical direction by identifying gaps, proposing improvements, and evolving the platform to support Jobber’s growth and future ambitions.
• Collaborate with upstream and downstream teams to ensure high-quality data flows through the platform, including reliable ingestion, schema enforcement, and foundational transformations.
• Establish standards and guardrails that promote data integrity, consistency, and trust while enabling teams to build on top of the platform without duplicating effort.
• Team members participate in a one-week on-call rotation to support the reliability of our data and ML platforms that may include off business hours support.
• Incidents outside regular working hours are compensated with time off in lieu to support a healthy balance.
• Occasionally, planned maintenance or major changes may require weekend on-call coverage, with a strong focus on preparation and minimizing disruption.

Benefits

• A total compensation package that includes an extended health benefits package with fully paid premiums for both body and mind, matching in RRSP, TFSA or FHSA, and stock options.
• A dedicated Talent Development team and access to coaching, learning, and leadership programs to help you grow your career, reach your goals, and unlock your full potential.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed