About us:
Spring Financial is a Canadian financial technology company focused on making every day financial services simpler, faster, and more accessible.
We build technology that helps Canadians build credit, save money, and access lending products without unnecessary friction. Our platforms allow customers to apply and manage their finances online, by text, or over the phone, making the experience convenient and flexible.
Since launching in 2014, Spring has grown into one of Canada’s largest fintechs, with over 250,000+ product originations across credit-building products, personal lending, and mortgage solutions. We’re a fast-growing, product-driven team that values practical solutions, strong execution, and thoughtful collaboration. We give people ownership, trust them to make decisions, and focus on building systems that scale reliably.
If you’re interested in working on real-world fintech platforms used by hundreds of thousands of Canadians, Spring offers the opportunity to make a tangible impact through well-built technology.
NOTE: This is a full-time, permanent, hybrid position in downtown Vancouver. 3 set days in the office and 2 WFH.
About the role:
As a Senior Data Engineer at Spring, you are a technical leader and strategic problem-solver who architects high-impact, resilient, and scalable data infrastructure — working across our core stack of AWS, Snowflake, dbt, and Airflow to deliver pipelines and data platforms that are secure, observable, and aligned with business needs. This role owns the design and execution of complex data initiatives, navigating trade-offs between speed and sustainability and ensuring our data architecture scales with the business. A strong advocate for AI-augmented engineering, you'll shape both how the team builds (e.g., using AI for testing, schema discovery, and code generation) and what the platform enables (e.g., automated QA, natural language interfaces) — modeling responsible AI usage and helping others incorporate it thoughtfully into development workflows. Working closely with senior stakeholders across Finance, Risk, Product, Analytics, and Engineering, this role helps frame problems, define SLAs, manage risk, and align data architecture with business goals — influencing both platform strategy and organizational standards.
What you’ll do:
Platform & Pipeline Engineering
- Architect and lead delivery of scalable, secure, and reliable data pipelines and platform components across AWS and Snowflake
- Scope and drive initiatives to modernize legacy data flows, unify batch and streaming sources, and enable self-serve analytics across the organization.
- Define and enforce engineering standards around testing, observability, security, and CI/CD within data systems.
AI Integration
- Champion the integration of AI capabilities within data pipelines (e.g., anomaly detection, automated tagging) and within development practices (e.g., assisted testing, documentation).
- Stay current on how agentic and AI-driven workflows are changing data platform requirements, and help the team adapt accordingly.
Cross-Functional Partnership
- Collaborate with technical and business leaders to clarify ambiguous problems, assess risks, and propose scalable, extensible solutions.
- Partner with Analytics, ML, and Finance and other business teams to deliver data with appropriate latency, accuracy, and governance for their use cases.
- Translate between business needs and technical constraints clearly — comfortable in a room with both engineers and executives.
Technical Leadership
- Lead technical design and code reviews, and support the ongoing evolution of our data architecture.
- Take ownership of how the team approaches hard problems — setting a high bar for quality, documentation, and engineering craft without needing to be asked.
- Help less experienced engineers grow by sharing context, giving direct feedback, and modeling good technical judgment.
What we're looking for:
Required
- Expertise in designing and scaling data pipelines using Snowflake, and AWS-native tools (e.g., Glue, Lambda, Redshift, Step Functions)
- Strong experience with real-time data systems, including Kafka, Kinesis, Flink, or Spark Streaming
- Deep knowledge of data modeling, schema evolution, privacy-preserving design, and secure data architectures
- Fluency in Python and SQL; proficiency with infrastructure-as-code (e.g., Terraform or CDK)
- Track record of embedding AI into both platform functionality and development workflows
- Proven ability to align data engineering with business value, and to lead cross-functional initiatives from concept through delivery
- Excellent communication and collaboration skills; trusted advisor across engineering and business teams
- Demonstrated ability to mentor and influence engineers, set standards, and guide technical direction
Nice to have
- Experience with dbt or analytics engineering patterns.
- Familiarity with ML platform tooling or feature store design.
- Background in fintech, credit risk, or other regulated data environments.
What We Will Give You:
- Competitive annual salary ranging from $120,000 to $140,000, reflective of experience and impact.
- Comprehensive benefits package, including extended health, dental, and vision coverage — with 100% of monthly premiums covered by the Spring.
- GRSP matching program to support your long-term financial goals.
- A modern, collaborative workspace in the heart of downtown Vancouver.
- Ongoing career growth opportunities
Please note: Upon applying, our Talent Acquisition team will review your resume. If you qualify, we will reach out to learn more about your experience and answer any questions you may have about the role, benefits, compensation, and more. Due to high application volume, we may not be able to respond to everyone.
Thank you for your interest! We appreciate your time and look forward to reviewing your application!
