Data Engineer II

Spring Financial; Vancouver; Hybrid; CAD 95,000 - 115,000; Full time

Job summary

A leading company in the Canadian fintech sector is seeking a Data Engineer II to build and maintain data pipelines and ensure data availability for analytics and machine learning. This hybrid role in downtown Vancouver offers competitive compensation, a comprehensive benefits package, and opportunities for career growth.

Benefits

Competitive annual salary

Comprehensive benefits package

GRSP matching program

Transit allowance

Modern workspace in downtown Vancouver

Ongoing career growth opportunities

Qualifications

• Strong experience with Snowflake, DB2, and AWS data services.
• Proficient in Python and SQL with experience in data pipeline frameworks.
• Familiarity with streaming patterns using Kafka or Kinesis.

Responsibilities

• Design and maintain secure and scalable data pipelines across AWS and Snowflake.
• Collaborate with BI, ML, and Finance teams to clarify requirements.
• Mentor junior engineers and contribute to team design reviews.

Skills

Snowflake

Python

SQL

AWS services

Data modeling

Mentorship

AI tools

Job description

About us:

Spring Financial is revolutionizing financial access for Canadians, providing smart credit-building, mortgage, and lending solutions. Millions struggle with high-interest debt and limited financial options—we’re here to change that.
As one of Canada’s fastest-growing fintech companies, annually we help 1 million customers explore their financing options with ease—online, via text, or over the phone. Our dynamic, innovative team thrives on collaboration, growth, and making a real impact.
To learn more about our products please visit our website here: www.springfinancial.ca.

NOTE: This is a full-time, permanent, hybrid position in downtown Vancouver. 3 set days in the office and 2 WFH.

Job Overview:

As a Data Engineer II at Spring, you are an experienced builder who takes ownership of the pipelines and platforms that power analytics, operational reporting, machine learning, and real-time product features. You work confidently across batch and streaming architectures, using tools like Airflow, Spark, Glue, Kinesis, and Kafka—while also integrating with Snowflake, AWS-native services, and legacy sources like DB2.

Your work extends beyond implementation: you help shape pipeline architecture, evaluate trade-offs between latency and scalability, and ensure data is available, reliable, and usable for a variety of internal stakeholders. You understand how to optimize data structures for analytical queries, power feature stores for ML models, and deliver reproducible workflows across test and production environments.

You’re responsible for designing and delivering full-stack data workflows across ingestion, transformation, validation, and observability. You build durable, testable code in Python and SQL, and leverage infrastructure-as-code and CI/CD practices to manage deployment and change. You use AI tools to speed up routine development, identify potential issues early, and assist in testing and documentation—while maintaining a critical eye for correctness and security.

You work directly with stakeholders from Data Science, BI, Finance, and Product, translating business and analytical needs into technical deliverables. You help clarify scope, identify technical constraints, and proactively communicate trade-offs and timelines. You also mentor junior engineers, review designs, and raise the team’s bar for quality and ownership.

What you’ll do:

• Design, implement, and maintain secure and scalable pipelines across AWS (e.g., Glue, Lambda, Step Functions), Snowflake, DB2, and streaming platforms like Kafka or Kinesis
• Develop and optimize data models, schemas, and transformations to support downstream analytics, ML, and reporting
• Operate orchestration tools such as Airflow to coordinate complex workflows and manage SLAs
• Use AI tools to assist with coding, test scaffolding, data profiling, and documentation—ensuring results are validated and safe
• Collaborate directly with BI, ML, and Finance teams to clarify requirements and deliver business-impacting solutions
• Troubleshoot and resolve issues in production pipelines, improving system observability, data quality checks, and alerting
• Mentor junior engineers and contribute to team design reviews, engineering practices, and technical planning

Requirements:

• Strong experience with Snowflake, DB2, and AWS data services (e.g., Glue, S3, Lambda, Redshift)
• Proficient in Python and SQL with experience in data pipeline frameworks, orchestration, and distributed compute (e.g., Spark)
• Familiarity with streaming patterns using Kafka or Kinesis
• Experience designing data models for BI, operational analytics, and ML feature stores
• Effective use of AI tools in development and testing workflows
• Proven ability to translate stakeholder needs into scalable data systems
• Comfortable mentoring others and contributing to design reviews and system evolution

What We Will Give You:

• Competitive annual salary ranging from $95,000 to $115,000, 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.
• Transit-Friendly Employer (Transit allowance).
• A modern, collaborative workspace in the heart of downtown Vancouver.
• Ongoing career growth opportunities

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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!