Clients I’ve worked with

AlphaForge, Cohort 2
1 of 50 selected from 900+

GTM Engineer · San Francisco

I build data and AI systems growth teams run on.

I come at GTM from the data engineering side — pipelines, models, and the plumbing that keeps them standing.

See what I’ve built Email me

Data engineering
Analytics consulting

Primary role: GTM Engineer
Focus: Data and AI systems
Location: San Francisco, CA

Things I’ve built

Discloser

Masumi

RankonChat

Content Engine

Maester

Selected work · 01

Three things I’ve built.

What was broken, what I did, what happened.

Own product · California real estate

Discloser

Open product ↗

The problem

A single California property can come with 200+ pages of disclosures.

Reading them by hand takes 45 to 90 minutes.

What I did

I sat with licensed agents for two weeks before writing a line of code.

Discloser flags the risks, estimates the cost, and cites the page it came from.

What happened

It gets through a packet in two to four minutes.

Agents use it in the field, on real deals.

2–4 min: Review time
200+ pages: Source packet
2 weeks: Field research

Enterprise work through DEPT

eBay and Masumi

View source ↗

The problem

eBay’s social data came in from markets all over the world.

None of it shared a common model.

What I did

I built the data layer on Airflow, dbt, and BigQuery.

Then Masumi, plus a creative analysis tool on top of it.

What happened

Masumi is in testing at DEPT, across client engagements at eBay and Meta.

The creative tool breaks down scripts and hooks second by second.

2 tools: Masumi and creative analysis
In testing: eBay and Meta engagements
Global: Social data scope

Clay AlphaForge project

ACL Cables

The problem

ACL Cables wanted to grow export revenue.

The buyers they needed were hard to find online.

What I did

I read ten annual reports and dug through trade data.

Then defined the buyer profile, the segments, and the rules for ruling someone out.

What happened

The system kept 720 companies and cut 91.

Every cut has a reason on record.

720: Qualified companies
91: Recorded removals
29: Fields per company

Selected experience · 02

Where I’ve done this before.

Salesforce

Built product health models across legacy and acquired brands — Slack and MuleSoft among them.

Snowflake · Airflow

Twitch

Ran social intelligence for teams across APAC, the Americas, and Europe.

Airflow · dbt · BigQuery

Intuit

Audited the marketing stack and worked through hundreds of keywords over tax season.

Marketing data

Google Chrome

Pulled marketing, ad, and keyword data together in BigQuery and PLX.

BigQuery · PLX

Method · 03

How I work (in a nutshell)

One system that turns what the business knows into accounts worth calling.

Customer calls Workflows Source data

01 Discovery

Define the business logic.

Learn how the business actually decides. Turn those rules into code.

Machine-readable rules Structured repository

02 Validation

Test a small sample.

Run every enrichment on a sample before it touches the full audience.

MeasureHit rate ReviewIncorrect results

03 Routing

Match data to the audience.

Each ICP gets its own evidence.

Importer-distributor Manufacturer Contractor

Output Qualified accounts with supporting evidence

Contact · 04

Let’s talk.

I’m looking for GTM engineering roles.

I’m also up for AI systems work that puts me close to actual customers.

Email nimchrisryansilva@gmail.com

GitHub ↗ LinkedIn ↗ X ↗