AI Ships Features. I Ship Direction.
Data & Platform Product Manager who builds the systems that make business performance legible. Owned data architecture, attribution, and activation platforms behind mission-critical growth engines and consumer workflows. Founder experience diagnosing ecosystem-level data lineage failures.
Echo Paulus
Product Manager, Data & Platform
How I Think About Data Systems
Modern products are data systems before they are interfaces.
Every UI, workflow, and activation layer depends on whether the right data reaches the right place at the right time. When events are inconsistent, delayed, or poorly modeled, the product fails — no matter how polished the interface looks.
My work focuses on designing clean event schemas, reliable ingestion pipelines, and explicit data contracts between upstream and downstream systems. When data is structured correctly and ownership is clear, product behavior becomes predictable, scalable, and trustworthy. Good products aren’t powered by dashboards — they’re powered by data systems that enforce integrity by design.
What Shaped This Perspective
- Dishclosure
UX innovation couldn’t compensate for missing or unreliable upstream data. The real constraint wasn’t the interface — it was the data model underneath it.
- Gainbridge
Clean event modeling, dependency mapping, and taxonomy governance unlocked entire workflows and aligned product, engineering, and marketing around a shared system of truth.
How This Shows Up in My Work
Start with the data model. Enforce the contracts. Let every feature flow from there.
Featured Projects
A few representative case studies. Each one focuses on system clarity: definitions, contracts, and decisions that scale.
Gainbridge: Product Manager, Data and Measurement
Built the instrumentation layer that made performance trustworthy — before optimization.
Dishclosure: Founder and Product Lead
An operator-first investigation that revealed the real constraint wasn't UX or adoption, but broken upstream data lineage.
Lyft: Quality Engineer → Developer Experience PM
When to stop optimizing for conventional metrics — and start optimizing what actually matters.
How I work: build clarity inside messy systems
I align teams around shared definitions and reliable signals, then turn that clarity into execution that compounds.
- ELT + event flow mapping
- Ownership & handoff gaps
- Source → warehouse → activation
- Event taxonomy & schemas
- Lifecycle definitions
- Data contracts
- Diagrams + docs people actually read
- Demos, walkthroughs, alignment reviews
- Metric misuse callouts
- Avoid unnecessary features
- Ship higher-confidence changes
“Once the foundation is sound, execution becomes obvious.”
- ①Map the system
- ELT + event flow mapping
- Ownership & handoff gaps
- Source → warehouse → activation
- ②Define contracts
- Event taxonomy & schemas
- Lifecycle definitions
- Data contracts
- ③Socialize & align
- Diagrams + docs people actually read
- Demos, walkthroughs, alignment reviews
- Metric misuse callouts
- ④Build
- Avoid unnecessary features
- Ship higher-confidence changes
“Once the foundation is sound, execution becomes obvious.”