Data Platform Engineer II

Spring Financial

. Build and maintain scalable, secure, and reliable data pipelines and platform components across AWS and Snowflake .

Posted 9/21/2026full-timeVancouver • CanadaMid-LevelSenior💰 CA$90,000 - CA$120,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining scalable data pipelines using AWS and Snowflake, with a strong focus on integrating AI capabilities and ensuring data governance. Proficient in Python and SQL, with a solid understanding of data modeling and real-time data systems.

Highest-signal resume keywords

AWS Data Pipeline DevelopmentSnowflake Data ManagementReal-Time Data Systems (Kafka, Kinesis, Flink, Spark Streaming)Python and SQL ProficiencyAI Integration in Data Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData ModelingSchema EvolutionInfrastructure-as-Code (Terraform, CDK)AI Tools ApplicationBatch and Streaming Data IntegrationCI/CD PracticesObservability StandardsAnomaly DetectionAutomated Tagging

Soft Skills

Strong CommunicationCollaboration SkillsSelf-Directed in AmbiguityProblem-SolvingAdaptability

Tools & Technologies

AWS GlueAWS LambdaAWS RedshiftAWS Step FunctionsKafkaKinesisFlinkSpark StreamingDbtML Platform Tooling

Industry Keywords

FintechCredit RiskRegulated Data EnvironmentsData GovernancePrivacy-Conscious Data Design

Tech Stack

Tools & technologies

Amazon RedshiftAWSKafkaPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Build and maintain scalable, secure, and reliable data pipelines and platform components across AWS and Snowflake
Scope and deliver initiatives that modernize legacy data flows, integrate batch and streaming sources, and enable self-serve analytics
Apply and improve engineering standards around testing, observability, security, and CI/CD within data systems
Build AI capabilities into data pipelines, including anomaly detection and automated tagging
Use AI in development practices, including assisted testing and documentation
Work with engineers and business partners to clarify requirements, surface risks, and propose practical solutions
Partner with Analytics, ML, Finance, and other business teams to deliver data meeting latency, accuracy, and governance needs
Communicate technical trade-offs and work constraints to non-technical partners
Contribute to technical design discussions, code reviews, and the evolution of data architecture
Own problems through production while maintaining quality, documentation, and engineering standards

Requirements

What you’ll need

Solid experience building data pipelines using Snowflake and AWS-native tools (e.g., Glue, Lambda, Redshift, Step Functions)
Working experience with real-time data systems such as Kafka, Kinesis, Flink, or Spark Streaming
Good working knowledge of data modeling, schema evolution, and secure, privacy-conscious data design
Fluency in Python and SQL
Some exposure to infrastructure-as-code (e.g., Terraform or CDK)
Practical use of AI tools in your development workflow
Interest in building AI into the platform itself
Track record of delivering projects end to end, with an eye on the business value behind them
Strong communication and collaboration skills
Self-directed in ambiguity: comfortable asking good questions, acting on feedback, and taking on broader scope over time
Nice to have: experience with dbt or analytics engineering patterns
Nice to have: familiarity with ML platform tooling or feature store design
Nice to have: background in fintech, credit risk, or other regulated data environments

Benefits

Comp & perks

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
Modern, collaborative workspace in the heart of downtown Vancouver
Ongoing career growth opportunities
Hybrid work arrangement: 3 set days in the office and 2 WFH