AWS S3 Tables
0→1 in 8 weeksCustomers were spending millions building and maintaining complex infrastructure for AI/ML workloads. I led a 20-person team to design S3 Tables, a new product 
to eliminate that complexity, from concept to launch in 8 weeks.
S3 Tables, featured in the 2024 AWS CEO keynote, handles infrastructure automatically, providing a seamless console experience that lets teams turn big data into insights in seconds.
01 Who and why. Customers 
don't want to store structured data in unstructured storage while maintaining custom infrastructure. They need a solution to streamline storage operations so teams can focus on data querying.
Research
I used internal AI tools to synthesize transcripts from 20+ enterprise customer interviews. The synthesis surfaced two target personas and their core pain points.
Target personas
Data engineers maintain storage systems daily. Strategic leads evaluate infrastructure costs and reliability.
Pain points
Millions wasted on custom infrastructure and integration with query engines, while no native way to keep structured data up-to-date.
02 Product scoping. I adapted the AWS JTBD framework to give the team a shared language for strategic scope trade-offs. We categorized every user action and API details into 6 workflows and aligned on a launch plan in one week.
JTBD Framework
I first worked with the team to identify all user stories based on the JTBD framework, then mapped each story with its console steps, preconditions, and APIs to six groups: Create, List, View, Manage, Audit, and Delete.
Action plan
The framework allowed the team to align on a prioritized action plan with defined APIs, known limitations, and console impact, turning an ambiguous product space into a concrete roadmap.
03 Design Iteration. Integration across multiple services needs to be seamless. After testing three options, I persuaded the team to combine integration with table bucket creation as a default-on setting. 97% of customers never turned it off.
Exploration
I tested three integration models: a multi-step wizard that walked through each service, a fragmented approach with separate configuration pages, and a single-page create flow with integration built in.
Trade-offs
The wizard added friction to what should feel instant. The fragmented model scattered a single decision across multiple pages. Customer research showed most users' end goal was querying, so bundling integration into table bucket creation matched their mental model.
Decision
I proposed combining integration into the create flow as a default-on checkbox. One click replaces what used to require configuring multiple services independently. The team aligned quickly once the testing data backed it up.
04 Outcome
S3 Tables, launched as the top announcement at AWS re:Invent 2024, gives customers structured data storage with built-in query support, eliminating million-dollar custom infrastructure.
Unified data storage for AI/ML workloads
The S3 Tables console enables customers to create, manage, and query structured data for analytics and AI/ML workloads in a few clicks, drastically simplifies the way customers manage their storage.
Seamless Integration
What previously required custom-built infrastructure is now handled automatically. Integration across multiple AWS services is reduced to a single click during table bucket creation.
One click from data to insights
Once created, customers manage their tables from a single console. Table bucket details, permission controls, and storage settings are all accessible without switching between services.
Biggest launch for S3
S3 Tables launched at AWS re:Invent 2024, featured as the top announcement in AWS CEO's keynote.
Customers were managing storage infrastructure across three separate consoles by repeating the same setup tasks, with no unified way to access data stored in different storages. I led a cross-functional team of 10 to design a cohesive console experience that unified setup and access across all three services.
Launched at AWS re:Invent 2025, the unified console experience cuts cross-service infrastructure setup to a single guided flow, allowing customers to scale access for large datasets in seconds.