Senior Data Analyst (Fraud)

About Super.com We started Super.com to help maximize lives – both the lives of our customers and the lives of our team – so that everyone can experience all that life has to offer. For our employees, our promise is that Super.com is more than just a job; it’s an opportunity to unlock one’s potential, where learning is celebrated and impact is realized. We are more than a fast-paced, high-growth tech company; we care about our people and take career progression seriously. This is your career and our aim is to supercharge it through the people, the work, and the programs that fuel who we are. About the role Our data analysts are embedded team members, and work alongside PM’s, software engineers and designers in our mission aligned team (MAT) model. This allows our team members to gain deep experience and context into vertical-specific business problems, quickly growing into subject matter experts.  This position offers a dynamic and impactful opportunity to assume ownership of your work as you conduct in-depth analyses and productionize your work. This role specifically will work with user and transaction-level data to proactively detect and prevent earnings fraud. You’ll build and maintain fraud monitoring dashboards, investigate suspicious activity, and collaborate with cross-functional stakeholders to develop, test, and improve our fraud rules. You’ll communicate insights, support incident response, and ensure data quality to minimize financial loss and protect safe users. About the team This role is part of our Analytics & Data Science team. Being Data Driven is a core value of Super.com and this team plays a huge part in this. We are long time advocates and users of Snowflake, DBT & Amplitude. This isn’t a place where we are convincing the business the value of data or just using these tools for the first time. We have maturity in our technical practices, launch a new experiment almost every day, and we are looking for teammates to roll up their sleeves and do high quality data work. What you’ll be working on:

Analyzing complex datasets to identify patterns of fraudulent behaviour and generate actionable insights that reduce earnings fraud

Building and maintaining dashboards and reporting solutions for fraud monitoring and self-serve analytics

Collaborating with cross-functional teams to scope, prioritize, and deliver analytics projects that inform product roadmap and operational decisions

Investigating anomalies and support root cause analysis for fraud incidents

Supporting development and testing of fraud rules: help define objectives, use data to inform and optimize rules, analyze A/B tests, and measure impact on key metrics

Advocating for data quality, governance, and best practices to ensure consistency, reliability, and security of our data assets

Communicating findings clearly to technical and non-technical stakeholders across written and verbal channels, while documenting processes and results

Participating in weekly fraud syncs, sprint planning, and cross-team channels to stay aligned on priorities and incident response

Independently identifying high-impact business problems and develop the right data-driven solutions

What we’re looking for:

5+ years of experience in data or analytics roles. Bonus if part of that time was spent in fraud, risk, trust & safety, or credit/collections domains

Advanced proficiency in SQL for data analysis, including building production-ready queries, data models, and analytics workflows

Experience working with complex datasets in modern data warehouse and transformation tools (e.g., Snowflake, dbt) and at least one BI/dashboarding tool (e.g. Looker, Amplitude, Tableau)

Proven track record of owning ambiguous problem spaces end-to-end: scoping questions, defining clear success metrics, validating data quality, and iterating based on stakeholder feedback

Excellent communication and data storytelling skills, able to translate complex analysis into clear, actionable recommendations for both technical and non-technical audiences

Demonstrated ability to partner cross-functionally including prioritizing competing requests and aligning stakeholders on next steps and trade-offs

High degree of ownership and urgency, comfortable working in a fast-paced environment where fraud patterns evolve quickly and decisions have meaningful financial impact

Comfortability working with messy, event-level product data across mobile and web, tracing end-to-end user journeys and debugging tracking or data quality issues with engineers