Senior Data Analyst, Risk

Senior Data Analyst, Risk

Spring Financial is a Canadian financial technology company focused on making everyday 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 days in the office and 2 days WFH.

As a Senior Data Analyst in Credit Risk at Spring, you operate at the intersection of analytics, data engineering, and business strategy. You bring together advanced analytical capability with technical depth to build the insights, pipelines, and frameworks that power our credit decisioning, forecasting, and portfolio monitoring. You take full ownership of the analytical lifecycle, from shaping ambiguous questions to delivering strategic recommendations that influence credit policy, product direction, and financial outcomes.

You work across Snowflake, AWS, SQL, Python, BI tools,CONTACTS, and AI tooling to uncover insights,apo modernize data flows, and support high‑quality decision‑making across the organization.

• Architect and guide the delivery of scalable, reliable data pipelines and analytical workflows supporting credit risk use cases across AWS and Snow মোৰ.
• Create advanced dashboards and reporting in Tableau, Hex, Power BI, or Qlik Sense for executive visibility and operational monitoring.
• Analyze (§) credit bureau data, market trends, and internal portfolio performance to surface insights that influence underwriting, pricing, and collection strategy.
• Lead analytical projects from scoping through delivery, translating complex questions into structured frameworks and actionable recommendations.
• Modernize legacy data flows, unify batch and streaming sources, and improve data models to support self‑serveопол analytics and ML initiatives.
• Collaborate with engineering, analytics, product, and finance teams to clarify ambiguous problems, assess risks, and propose scalable solutions.
• Use Python and AI tools to perform advanced exploratory analysis, build predictive insights, and validate statistical outputs.
• Partner with ML and Finance teams to ensure datasets meet the appropriate latency, accuracy, and governance standards.
• Present findings and strategic recommendations to senior leadership, influencing direction and decisions across credit and product lines.
• Mentor analysts and contribute to elevating Spring’s analytical rigor, documentation standards, and data‑driven culture.

• Proven experience in data analytics with exposure to credit risk, consumer lending, fintech, banking, or financial‑services analytics.
• Strong proficiency in SQL and Python for analytical workflows, modeling, and exploration.
• Advanced skills in BI tools such as Tableau, Hex, Power BI, or Qlik Sense, with experience creating reports for senior management.
• Experience working with cloud data platforms such as Snowflake and AWS.
• Strong understanding of data modeling, schema evolution, and how pipeline design impacts downstream analytics.
• Ability to independently lead cross‑functional initiatives and align analytical work with business value.
• Excellent communication skills and the ability to present insights clearly to senior stakeholders.
• Comfort using AI tools to accelerate analysis, forecasting, segmentation, and hypothesis generation with…