Building a Self-Serve Data Catalog

Centralized, validated, searchable data documentation in Looker. Turned a scattered, confusing data landscape into a self-service resource for the whole company.

↓
time to resolve data requests
↑
new-hire self-service, no data-team bottleneck
20
internal customer interviews, with repeated, unprompted requests for a "single source of truth"

3 weeks to v2

Week 0
Kickoff: vendor "build vs. buy" evaluation begins
Week 1
Discovery: metadata mapped across the full data ecosystem
Week 2
Ship v1: Looker catalog live, basic filters
Week 3
Feedback: users flag v1 as "confusing and overwhelming"
Next
Ship v2: restructured by user need, feedback loop added, simplified main UX

What the catalog holds

Data glossary
every event + table, defined
Documentation
models + FAQs, linked
Data assets
source + type, discoverable
Lineage
(soon) pipeline map
Quality + health
live monitoring status
Taxonomy
naming + classification

The risk, and what happened

Risk	How it was handled	Result
Usability. Data documentation was scattered across formats. Every data request required manual triage, slowing onboarding.	Scoped a lightweight ETL with the metadata already being gathered for the vendor evaluation. Shipped v1 in Looker in 2 weeks.	Feedback interviews called v1 "confusing". Rebuilt into separate views by user need in v2.

My role. Owned the build-vs-buy evaluation and scoped the metadata ETL as the validation vehicle. Ran the vendor conversations. Made the call to ship a stopgap rather than wait on procurement.

With engineering. Partnered directly with a data engineer on the ETL design. Chose speed of extraction over completeness of metadata coverage to get a testable v1 in front of users faster, backfilling coverage in v2.

Product leader with a data scientist past · Zapier, Clearbit, HubSpot

Product doc: ale-cabrera.com/portfolio/data-catalog