PM — Versatile
Product Owner/Manager

Defining
what's worth
building.

5+ years taking products from ambiguous opportunity to shipped reality; across enterprise, startup, and research environments. Engineering background that earns trust with dev teams. Product instinct that earns trust with stakeholders.

5+ Years PM/PO
6 Products Shipped
3 Industries
Product Strategy AI Integration SaaS IoT Agile / Scrum Discovery & Validation Stakeholder Management Go-to-Market
Product work
waterdave81@gmail.com +52 443 479 0466 Monterrey, MX EN / ES — Remote

Projects

01 Enterprise / Embedded Software
John Deere — Global Markets

Suspension Control System for Sprayers

Chassis & PowerTrain domain. A high-clearance sprayer engineered for Brazil's steep agricultural terrain; requiring deep discovery with field operators and cross-functional delivery across global engineering teams.

The Challenge
Translate real-world field constraints from Brazilian operators into precise embedded software requirements, while coordinating delivery across multicultural teams in multiple time zones.
The Outcome
Suspension transition performance improved by 26%. Product on track for global launch in 2027.
↑ 26% suspension transition performance
John Deere Sprayer
02 Consultant/ Embedded AI
REEFAC — Reef Aquaculture Conservancy

Autonomous Reef Monitoring System

An autonomous AI computer vision system for reef biodiversity monitoring — deployed underwater for researchers and governments. Built from first principles under real marine constraints.

The Challenge
The device's underwater installation location made maintenance access extremely complex; maximum operational autonomy was a hard requirement, not a preference.
The Outcome
AI CV camera with deep sleep power strategy delivering ~15 days of battery life in real underwater deployment conditions. Launched and operational in 2025.
~15 days battery life in field conditions
Underwater reef
03 Consultant/ Market Research
REEFAC — Climate Tech

Computer Vision Platform Go-to-Market Strategy

Market research and investor narrative for an embedded computer vision platform targeting smart aquaculture, biodiversity monitoring, and environmental surveillance, sectors with combined markets exceeding $50B by 2030.

The Challenge
Translate deeply technical embedded AI constraints into a compelling investment narrative for international audiences; Asian and Canadian investors unfamiliar with field-deployed CV systems.
The Outcome
Full investor pitch deck: problem framing, $50B+ market sizing, architecture narrative, GTM strategy, and revenue model. Delivered to international investors in 2025.
$50B+ addressable market identified
Ocean technology
04 Startup / IoT
STG

Remote IoT Air Quality Data Logger

A solar-powered autonomous IoT device for real-time environmental monitoring — designed for governments in México to measure air quality across polluted cities without depending on stable power or connectivity infrastructure.

The Challenge
Deliver a low-cost, high-precision system capable of reliable remote deployment anywhere — translating a public policy need into a concrete hardware product with measurable data quality requirements.
The Outcome
Remote device transmitting reliable data via MQTT with configurable sampling rate. ML model PoC improved data accuracy from 79% to 85%. Deployed in production with SIMA.
79% → 85% data accuracy via ML pipeline
IoT sensor in field
05 Startup / SaaS
STG

Sensing as a Service — Data Platform

A SaaS platform to host, visualize, and analyze data from distributed IoT sensors — translating raw environmental data into actionable intelligence for government users.

The Challenge
Design a user-friendly interface for non-technical government stakeholders that could handle high-frequency sensor data, geospatial visualization, and multi-organization user management at scale.
The Outcome
Scalable architecture with full API integration, real-time geospatial pollution mapping, and user management. Live in production with Consejo de Nuevo León.
↗ Live in production — government users
Data dashboard
06 Startup / AI
STG

LLM Agent for Air Quality Analysis

An autonomous LLM-based agent that connects to the platform API, analyzes real-time sensor data, and delivers environmental insights in natural language; making complex data accessible to non-technical users.

The Challenge
Build an AI agent that synthesizes complex multi-sensor data with minimum resource consumption, and define evaluation criteria for consistency, hallucination rate, and factual accuracy before production deployment.
The Outcome
Multiple LLM models under structured evaluation — assessing behavioral alignment against real sensor ground truth. Iterating on prompts, guardrails, and agent instructions.
AI visualization

Product thinking
with an engineer's
instinct.

I started as an embedded software engineer, building systems where a bad call had real consequences. That shaped how I approach product: with precision, with respect for technical constraints, and with a bias toward shipping things that actually work.

Today I work across the full product lifecycle; from discovery and strategy to delivery, go-to-market, and AI integration. I'm most effective in environments where the problem is ambiguous and the stakes are real.

B.Eng. Electronics & Digital Systems — TECNM, 2021.
Monterrey, MX. Open to remote international roles.

Product
  • Vision & Roadmap
  • User Discovery
  • Backlog Management
  • Go-to-Market Strategy
  • Stakeholder Alignment
  • KPI Design & Tracking
AI & Technical
  • AI Integration
  • Business Intelligence
  • IoT Systems
  • Data Analysis
  • Embedded Systems
  • SaaS Architecture
Methods & Tools
  • Agile / Scrum
  • OKRs & Metrics
  • Root Cause Analysis
  • Linear · Azure DevOps
  • Tableau · Power BI
  • Notion · Slack
Languages
  • Spanish — Native
  • English — C1 Advanced
  • Portuguese — A1
  • Hebrew — A2