CTPO who ships AI-native products and platforms. I build high-performing teams, turn data into leverage, and connect technology to business outcomes. Patents in ML/AI (pre-gen-AI), mobile app roots (50+ apps shipped), and a track record scaling companies through venture and PE.

Over the years I found myself re-explaining how I work, my values, operating cadence, and what “good” looks like. This is the field guide for anyone working with me: engineering, product, data, go-to-market, or exec partners.

• Degrees: BS Computer Science (Fairfield), MS Computer Engineering (Boston University, Software Eng focus).
• Range: BigCo (Raytheon) → early-stage → Series A/B → PE.
• Builder first: Started as an IC (yes, even did ADA back in the day), then Android/iOS (shipped 50+ apps) → engineering/product/data leadership → CTPO.
• Patents: Multiple ML/AI patents before the gen-AI wave.
• Where I live: Boston. Celtics season ticket holder. Family, travel, beach reads.

CTPO @ Tatango. We’ve evolved from “messaging platform” to intelligent infrastructure for social impact—SMS, MMS, WhatsApp, and RCS—with AI-driven engagement at scale.

• AI inside the product & the SDLC: RAG for brand-safe content, Smart Send Time, Power Segments, predictive donor scoring, sentiment-aware messaging.
• Modern data layer: Snowflake + dbt + Lightdash + Prefect → single source of truth, faster experimentation, real-time decisioning.
• AI-native SDLC: automation, agents, and guardrails → 10× faster deployments, 90% fewer change failures.
• Trust/compliance: SOC 2 Type II, HIPAA; privacy and safety as product features.
• Revenue automation: usage-based billing capturing $60M+ annually.
• Go-to-market shift: sales-led → product-led, data-intelligent growth; nonprofit revenue up 39× (3,900%).

The job flexes with company stage. The best ones stay hands-on, translate strategy into operating systems, and make impact measurable.

• 0→1: Prove value fast. Buy vs. build ruthlessly. Thin slices, strong telemetry, zero ceremony.
• 1→N: Platformize. Create primitives, paved roads, and a metrics fabric.
• N→Scale/PE: Tighten economics (margins, reliability, compliance), invest in SDLC leverage, and build durable teams.

Technology is a profit center. I align engineering to P&L and customer outcomes.

• First-principles trade-offs: speed vs. durability, capex vs. opex, build vs. buy.
• Observability everywhere: instrumentation before iteration.
• Short feedback loops: experiments that retire risk quickly.
• Sane defaults: opinionated platforms and paved roads that remove drag.

• Weekly: outcomes review (KPIs/OKRs), rollout board, risk ledger.
• Bi-weekly: product bets & learnings; experiment kill/scale decisions.
• Monthly: talent & org health, reliability, security posture, cost curves.
• Quarterly: portfolio re-allocation; what we’ll stop doing.

• BAR (Bet, Assumptions, Risk): every initiative has explicit assumptions and a kill switch.
• Guardrails > Gates: enforce quality with automation (tests, policies, feature flags), not meetings.
• “Measure twice, cut once” data model: analytics ready on day one.

1-pager problem briefs, PRDs with hypotheses, ADRs for irreversible decisions, runbooks for ops.

✅ Ownership & bias for action
✅ Clarity: plain English, no buzzword armor
✅ Craft: code quality, data modeling, design, security
✅ Teaching culture: leaders who create leaders
✅ Customer signal: talk to them, measure them, learn from them

🚫 “Ship then think”
🚫 “That’s not my job”
🚫 Hidden work, hidden metrics
🚫 Process for process’ sake

• High bar hiring: learning velocity > laundry list of tools.
• Small, autonomous teams: clear mission, tight interfaces, end-to-end ownership.
• Micro-feedback loops: weekly feedback > annual surprises.
• Career paths: IC and Manager tracks are equally valued.
• Psychological safety: dissent welcome; we commit once decided.

Teams that ship HOURLY, own the outcome, and can explain why their work moved a KPI.

Leadership ≠ distance from code. I’ll jump in to unblock and to learn.

• Architecture & platform choices (and the buy-vs-build math).
• Data & analytics: modeling, dbt reviews, metric definitions.
• Release engineering: flags, progressive delivery, rollback plans.
• Security & compliance as design constraints, not afterthoughts.
• Dirty work when resources are tight.

• Agents in the loop: scaffolding, tests, docs, PR reviews.
• Policy as code: guardrails for secrets, PII, accessibility, and perf budgets.
• Progressive delivery: feature flags, canaries, automatic rollback on SLO breaches.
• Telemetry by default: logs, metrics, traces, plus business KPIs stitched to releases.

Outcomes: 10× deploy speed, 90%+ reduction in change failures, faster MTTR, tighter product learning cycles.

• Start with the KPI. Every roadmap item has a target metric and an experiment plan.
• Thin-slice the experience. Hand off to customers early; let data choose the path.
• Benchmarks & insights: show customers where they stand and what to do next.
• Human-in-the-loop AI: brand-safe generation, explainability, and controls.

• Snowflake + dbt + Lightdash foundation: single source of truth; faster dashboards; fewer ad-hoc asks.
• RAG in production: on-brand, context-aware content at scale.
• Smart Send Time / Power Segments: materially higher engagement with behavioral signals.
• Usage-based billing: $40M+ captured annually, cleaner margins and predictability.
• SOC 2 Type II & HIPAA: shipped without slowing the roadmap.

I hire builders with strong communication, curiosity, and grit. I care more about how you think and learn than whether you’ve used tool X. Diversity of backgrounds and thought isn’t a nicety—it’s a scaling advantage.

Process: structured, transparent, signal-dense. No trick questions. Real problems, real collaboration, real feedback.

• Availability: quick feedback, unblocked teams.
• Communication: async by default; meetings are for decisions.
• Escalation: raise risks early—bad news compounds.
• Commitment: once we decide, we move as one.

My #1 responsibility is to the people. When teams thrive, products accelerate, customers feel it, and the business performs. I’ll keep evolving this doc; if you have feedback, I want it.

• Cloud/Infra: AWS (Lambda, Fargate, ECS, CDK), serverless, IaC
• Data: Snowflake, dbt, Lightdash, Prefect, Elasticsearch
• App: Node/Rails/.NET variants, React/Next, Swift, Java (iOS/Android)
• Messaging: SMS/MMS/WhatsApp/RCS; delivery optimization & compliance
• SDLC: CI/CD, feature flags, e2e testing, observability, security & privacy by design

CTPO building AI-native products and platforms. Patents in ML/AI, mobile roots (50+ apps), and a track record scaling companies through venture and PE. I turn data into leverage, teams into engines, and shipping into a habit. Always happy to talk AI/ML, product strategy, and scaling teams.