Meta — Daiquery/Bento notebooks
SQL and Python notebook creation and consolidation; data‑viz framework work; AI‑infographic hack project and augmented data workflows.
Focus
SQL notebooks work with outcomes that informed Meta AI image creation flows.
I spearheaded the initial creation and development of Daiquery notebooks, a concept involving a series of merged SQL query cells designed to enhance data analysis workflows. This approach allowed users to run and visualize SQL queries within a single interface, improving efficiency and usability.
This work laid the foundation for integrating these features with Bento notebooks, creating a cohesive and powerful data analysis tool. The merger involved extensive collaboration with engineers and PMs to harmonize functionality and provide a seamless user experience.
Throughout the project, I focused on both the interaction and visual design aspects, ensuring the notebooks were not only functional but also intuitive and visually appealing. Contributions included designing prototypes, conducting user research, and iterating on feedback to refine the final product.
Team dynamics
Worked closely with engineers to determine and implement the initial functionality around grouped cells. Presented and connected across the org to get the concept moving, and partnered with other designers to maintain consistency and enhance the user interface between this product space and others.
Initial creation of daiquery notebooks, a collection of SQL cells for various reasons
The initial goals were focused around the creation of a SQL cell‑based notebook as a way to better connect complex queries for storage and understanding by Data Scientist / Data Eng / Software Eng users.
Work began with discovering everyday problems across the org: connecting for feedback over workplace groups, interviewing users around their issues, and understanding the product and similar ones in the field.
‘Cells’ became a major focus for the next few years, with new types added, visual treatment shifting to accommodate additions, and comparisons with analogous notebooks outside the company.
discovery of common feature sets with nearby applications
As the product expanded, we discovered commonalities with other infra product spaces. We were an SQL notebook, but there was already a Python‑based notebook with similar features (Bento).
Daiquery came with a file cell while Bento had a file panel; this shifted discussions across the product: structure of cells, interactions around them, and where functionality should live (panel vs. cell).
expansion into additional application concepts
Early cross‑functional discussions (PM/ENG/DS) explored areas missing from both products that could benefit the workflow: an expandable plug‑in/app store, education, sharing, and more.
crafting and selling the org on a universal shared notebook
These explorations evolved into final prototypes and a narrative that connected groups and planning for the next half, arguing for a universal shared notebook direction.
company changes, shift of focus while continuing to support users
Org shifts reduced scope; broader education/documentation goals were deferred. Focus returned to the technical audience while continuing to support users.
Users also used notebooks for notes, which informed a workflow to upgrade text cells toward a more doc‑like cell type.
ai as a chatbot vs ai as a static cell type
AI entered almost every discussion: surfacing join recommendations or related data objects for SQL/Python users.
Explored whether users preferred in‑situ recommendations vs. a helpful bot, and whether they wanted to compare results or have code produce results and move on.