Senior Product Manager · Seattle, WA

Building data platforms people actually trust.

I'm Danny — a people-first PM with 6+ years at P&G leading enterprise data products, AI-powered capabilities, and cross-functional teams across global organizations. My engineering and analytics background means I can go deep with technical teams — while staying obsessively focused on the humans who use what we build.

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Dhanush (Danny) Thota — Senior Product Manager
6+
Years in Product & Data
$2M+
Value Created at P&G
116%
Platform Performance Gain
300%
Faster Time-to-Resolution via AI
"The best products don't come from the smartest individual in the room — they come from teams who trust each other enough to build something none of them could alone."

How I Work

My PM Philosophy

Six principles I return to regardless of what I'm building, who I'm partnering with, or how murky the roadmap looks.

01

Discovery before direction

I spend a disproportionate amount of time in discovery — user interviews, usage pattern analysis, support ticket trends — before a roadmap takes shape. Skipping this step is where most bad products begin. It's where I uncovered the real performance bottlenecks at P&G before anyone opened a Jira ticket.

02

Focus on outcomes, not artifacts

Specs and PRDs are just tools. My real job is defining the success delta — the measurable difference in user behavior and business value — and ensuring the team has the autonomy to find the best path to get there.

03

Data-informed, not data-dependent

Quantitative data tells me what's happening. Qualitative research tells me why. Good decisions need both — and occasionally, the courage to move when the user signal is strong but the dataset is thin.

04

Experimentation is a muscle

I've built A/B testing culture at P&G — from experiment design and measurable hypothesis framing to statistical rigor and post-launch iteration loops. Teams that experiment consistently make better decisions over time, full stop.

05

Strategic restraint

The "Yes" must be earned; the "No" must be defended. In an era where AI has lowered the cost of building, the PM's most vital role is as a filter. I focus on protecting the product's core value by resisting feature bloat and hype-chasing — ensuring every roadmap item solves a high-stakes problem rather than just increasing the surface area of the product.

06

Build to learn, then build to scale

In a high-velocity environment, the spec is never the finished product. I leverage AI-assisted prototyping and iterative loops to fail safely and cheaply. This allows us to sharpen requirements through actual usage — ensuring that when we finally hit build, we are scaling a validated success.

Selected Work · Procter & Gamble

Projects that moved the needle

Three initiatives from my time at P&G — the problem, my process, and what we learned along the way.

AI · Enterprise Data · FastMart · 2024

Launching Data Studio — an AI-powered self-service capability that compressed change cycles from weeks to hours

Commercial teams at P&G were bottlenecked waiting weeks for data changes that should have taken hours. Through structured discovery with UX Research and Data Science, I found the real friction wasn't data access — it was the absence of guided self-serve tooling. I led the development of Data Studio, an AI-powered capability embedded in FastMart (P&G's enterprise data platform), enabling users to query and act on data without engineering dependency — while also implementing AI-driven chatbots and intelligent data pipelines that transformed operational throughput.

My Process

  • Led structured discovery with UX Research to map friction in commercial data workflows
  • Partnered with Data Science to define AI scope and validate feasibility before committing to build
  • Wrote PRD with measurable hypotheses and clear acceptance criteria per capability
  • Aligned eng, design, and commercial stakeholders through weekly structured syncs
  • Tracked adoption post-launch via automated dashboards and iterated on friction points

Outcomes

4 wks → 4 hrs
Change-management cycle compression
+35%
Customer satisfaction (CSAT) uplift
$1.2M
Global savings from AI-driven automation

What I'd do differently: I'd invest more in internal evangelism before launch. Several teams didn't discover the capability until weeks after GA.

Platform Migration · Data Engineering · 2023–2024

Driving a 116% platform performance improvement through discovery-led migration to Databricks Unity Catalog

FastMart users were experiencing sluggish query performance that was eroding platform trust across commercial and supply chain teams. Rather than defaulting to an infrastructure fix, I led user interviews across 15 sales teams to understand where performance degradation was actually felt — and what it was costing them day-to-day. Those insights revealed a mix of behavioral patterns and architectural bottlenecks, ultimately informing a migration to Databricks Unity Catalog and a new monitoring strategy that dramatically improved reliability and adoption.

My Process

  • Conducted user interviews across 15 sales teams to go beyond surface-level performance complaints
  • Synthesized findings with Data Science to separate behavioral from architectural root causes
  • Advised senior leadership on removing data governance barriers for POS data systems
  • Defined KPIs and success metrics before migration began — not after
  • Engineered automated monitoring dashboards to detect post-migration issues proactively

Outcomes

116%
Platform performance improvement
105%
Analytical user adoption increase
+22%
Service level improvement via automated monitoring

What I'd do differently: Pulling governance stakeholders in earlier would have shortened the compliance review by at least a sprint.

Internal Tooling · Self-Serve Analytics · 2022–2023

Building Analyst Sandbox — a governed self-serve environment that cut time-to-first-insight by 60%

Analysts across P&G had no safe environment for exploratory data work that didn't risk production systems. There was no standard onboarding, no shared resources, and no visibility into usage or cost. I created and led Analyst Sandbox from scratch — designing the full intake-to-offboarding flow, publishing golden datasets and starter kits, building usage and cost dashboards with alerting, and automating deprovisioning to eliminate a recurring manual ops burden. Adoption was driven by the documentation and resources, not the launch announcement.

My Process

  • Mapped the end-to-end analyst journey from access request to role-off to find all friction points
  • Designed role-based access, least-privilege policies, and environment templates for standardization
  • Shipped golden datasets, SQL patterns, and data contracts before the tool itself
  • Built usage, cost, and health dashboards with idle-resource alerts and overspend flags
  • Automated offboarding notifications and audit evidence capture — saving ~50 hrs/quarter

Outcomes

60%
Faster analyst time-to-first-insight
200 hrs/yr
Productivity savings via automated offboarding
$75K/yr
Saved through reduced support volume and runbook adoption

What I'd do differently: I'd replicate the documentation-first sequencing in every platform feature going forward — it's the highest-leverage adoption move I've made.

Toolkit

What I bring to the table

Core PM competencies shaped by enterprise data, AI, and consumer product work — paired with hands-on technical depth that lets me move faster with engineering and data science teams.

Product & Strategy

Product Strategy & Vision Roadmap Ownership AI & GenAI Productization PRD Development Experimentation & A/B Testing KPI & Metric Definition UX Research Partnership Customer Journey Mapping Go-to-Market & Change Management Cross-Functional Leadership Stakeholder Influence Data & Analytics Platforms Data Governance

Technical Proficiencies

SQL & SparkSQL Python Databricks MS Power Platform AWS KNIME SharePoint Active Directory Microsoft Office Suite

Education

M.S. Industrial & Operations Engineering, 2018
University of Michigan, Ann Arbor
B.S. Industrial & Systems Engineering, 2017
San Jose State University
Dean's Scholar, 2016–2018

Languages

English Telugu

The Human Behind the PRDs

A little bit about me

I believe who you are outside of work directly shapes how you show up in it. Here's what keeps me curious.

I came to product management through an unconventional path — industrial engineering, supply chain ops, enterprise data analytics — before landing in PM. That cross-disciplinary background is something I lean on constantly. I can sit with an engineer and talk data pipeline architecture, then walk into a leadership review and translate it into business impact without losing either audience.

I grew up bilingual — English and Telugu — which has shaped how I think about communication: clear, intentional, never assuming shared context. I bring that same instinct to PRDs, roadmaps, and stakeholder conversations. Outside of work, you'll find me at a track day, behind a camera, on a mountain, or overthinking a 4-foot putt.

🏎️
Motorsports & Cars
Obsessing over marginal gains and system-level thinking — same energy I bring to platform performance work
📷
Photography
Trains you to slow down and notice what's actually there — an underrated discovery skill for any PM
🏔️
Snowboarding & Longboarding
Routes never go as planned. You read the terrain and adapt in real time. Sounds familiar.
🏀
Basketball & Golf
One is about trusting your teammates before the ball moves. The other is a lesson in managing your own expectations.

What I'm currently thinking about

  • Exploring: How GenAI changes the economics of self-serve analytics — and what that means for enterprise data PM craft at scale
  • Building on: SQL and Python depth — I want to pull my own exploratory analyses without depending on a data engineer for every ad hoc question
  • Interested in: Data monetization strategy in B2B SaaS — how platforms like PitchBook and Crunchbase turn raw data into proprietary, defensible products
  • Background spanning: Consumer (Amazon, Kroger), CPG (P&G), and enterprise data platforms — looking to apply this breadth to a role where product and data are deeply intertwined
  • Open to: Senior PM or Lead PM roles in enterprise data, AI/ML platforms, or B2B SaaS where engineering and product genuinely co-create roadmaps together

Let's Talk

I'd love to hear what you're building.

Whether you're a recruiter, a founder, or a fellow PM who wants to swap notes on data products and AI — I'm always up for a good conversation. I try to respond within 24 hours.

💼 LinkedIn — Dhanush (Danny) Thota → 📄 Download Resume (PDF) →