This project is currently being edited. Please check back shortly for updates.

Snowflake Query Advisor

Transform Inefficiencies into Insights with Smarter Query Optimization

Please note that confidential information has been obscured or excluded from this case study.
The following views are my own and do not reflect the views of Capital One.

Background

In late 2020, Capital One made a bold move into the SaaS space, aiming to revolutionize how data is produced, consumed, and governed—giving rise to Capital One Software. Their flagship product, Slingshot, offers a comprehensive solution for optimizing and managing Snowflake data warehouses, empowering businesses to maximize the value of their data.

For Snowflake users, identifying query inefficiencies and implementing optimization recommendations isn’t just beneficial—it’s critical to unlocking peak performance and operational efficiency.


As the lead product designer, I led the design of the Query Advisor tool, crafting an intuitive in-app experience to help users identify query inefficiencies and seamlessly apply optimization suggestions.

Role
Lead product designer

Responsibilities
Research
Interaction design
Prototyping
Usability testing
Visual design

Team
Product designer
Product manager
Research
Engineering

Problem

Snowflake users often face a critical challenge: inefficient queries that lead to unnecessary costs, slower performance, and wasted resources. While optimization is essential, identifying inefficiencies and applying the right fixes can be time-consuming, complex, and error-prone. Without a streamlined solution, users are left navigating this process manually, hindering their ability to fully leverage the power of their data warehouse.

Previous
Previous

SMB Payroll Platform

Next
Next

Consumer Homesite