Analytics Engineer II

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 . Analytics Engineer II (L2) As an Analytics Engineer II at Spring, you are an experienced data practitioner who owns the data models that power analytics, operational reporting, and strategic decision-making. You are a key builder of our semantic layer and curated data marts, designing and extending them to new areas of the business. You work confidently with complex datasets, profiling and exploring data directly to shape it into performant, dimensional models. Your focus is on building scalable, governed data products that enable analysts and business teams to work efficiently and consistently. Beyond implementation, you help shape analytical approaches, evaluate trade-offs between competing metric definitions, and ensure our data products are accurate, performant, and easy to use. You know how to optimize data models and queries in Snowflake & dbt for performance, leveraging features like clustering keys, to ensure our BI dashboards are fast and responsive. You are responsible for designing and delivering full-stack analytical workflows – from modeling new data sources into dimensional/start schemas and semantic layer metrics, to enabling downstream analysis and decision-making. You ensure all models are durable, testable, and version-controlled through our CI/CD practices. You use AI tools to speed up routine analysis, identify anomalies, and assist in documentation – while maintaining a critical eye for accuracy and business context. You work directly with stakeholders across the organization to translate business needs into new or enhanced models. You proactively identify data gaps, validate source data, and communicate timelines clearly. You mentor junior engineers, review their work, and raise the team’s bar for quality and technical ownership. What you’ll do: Design and implement scalable dimensional models and curated data marts using snowflake and dbt Develop and maintain semantic layer metrics that are accurate, governed, and aligned to business definitions Write and optimize complex SQL queries and transformations for performance and reliability Partner directly with business stakeholders (e.g., in Finance, Marketing, Operations) to define KPIs and translate requirements into reusable data products Validate models through automated tests, data reconciliation, peer reviews, and observability practices Troubleshoot and resolve data issues in production models, improving data quality checks and alerts Use AI-assisted tools for development, anomaly detection, and documentation – validating all outputs for correctness and context Mentor junior analytics engineers and contribute to team design reviews, best practices, and technical planning. Requirements: Strong experience with modern analytics tools (e.g., dbt, Hex, Tableau, Snowflake) Advanced proficiency in SQL, including window functions, CTEs, and performance tuning Experience designing and implementing data models for BI and operational analytics (e.g., star schemas) Hands-on experience developing, testing, and documenting models in dbt Ability to translate stakeholder requirements into well-modeled, reusable data products Proven ability to validate and troubleshoot data issues using testing frameworks and observability tools Effective use of AI-assisted tools in analytical and development workflows Comfortable mentoring others and contributing to design reviews and technical standards