Data Products | ex-AWS

Tushar Sharma

I ship AI-native products teams actually adopt — semantic layers, agent tools, and the instrumentation that proves what works.

Currently exploring senior Product, Data Product, and AI Product roles.

Tushar Sharma
San Francisco Bay Area ttsharma018@gmail.com LinkedIn
Supio
100%
PM adoption of the tool I shipped
Every product manager at Supio uses the AI intelligence tool I scoped and shipped.
AWS
$82M
unmanaged revenue surfaced
AWS customer-mapping dashboard exposed spend that had no account owner.
Supio
<5 min
from question to insight
AI-native semantic layer at Supio replaced 24-hour analyst Q&A across Sales, CS, RevOps, Ops, and Product.
Supio
$32M
annual revenue supported
Billing infrastructure I product-managed runs invoicing end to end on a third-party metering platform.

About

I'm a product manager who builds AI-native products — and came up through data engineering, so I scope AI knowing exactly what the data can and can't do. As the founding data hire at Supio, I shipped an AI semantic layer that answers ~700 questions a week and an agent tool every PM there uses daily. The through-line in my work is judgment about when AI is the answer — and when it isn't: my biggest north-star win was a two-week UX fix with zero model changes.

How I Operate

Instrument before you model.

If the metric that matters isn't tracked, that's the first product gap to close. Every good decision I've shipped started life as a measurement problem.

When UX is the bottleneck, don't add a model.

My biggest north-star win was a two-week UX fix with zero model changes. AI judgment includes knowing when AI isn't the answer.

Self-serve beats service.

Queues don't scale; systems do. The best analytics product removes the analyst from the loop — including when the analyst is me.

In immature categories, build thin on open source.

Committing your metric layer to an unstandardized vendor category is the most expensive lock-in. Keep the definitions yours; swap the serving layer when the market matures.

Case Studies

Case study

AI-Native Semantic Layer

Operational data was locked behind a 24-hour analyst queue; new reports took days. I owned an AI-native semantic layer end to end: dbt marts plus YAML specs on DuckDB, served through an MCP server and queried in the Claude desktop app. Drove adoption across Sales, Customer Success, RevOps, Operations, and Product. Insight time collapsed from 24 hours to minutes, and two analyst hires per business function were avoided.

AI-Native Architecture Semantic Layer Self-Serve
Read the case study →
Time from question to insight
BI + analyst queue 100
Semantic layer + Claude 2
Case study

PM Intelligence Tool

PMs had no direct line to customer intelligence scattered across Gong, Slack, Notion, and Intercom. I scoped and shipped an AI tool that surfaces it, with agents for signal detection, analysis, and PRD generation. Adopted by every PM, it cut customer-research time roughly 60% and PRD drafting from hours to minutes.

0→1 Product AI Agents Adoption
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Customer-research time
Before 100
After 40
Case study

Instrumentation, then Experimentation

Engagement metrics were everywhere; the metric tying product output to customer revenue wasn't tracked. The loop I run: find what's missing, instrument it, diagnose friction via user research, ship a scoped experiment. Marquee was a 2-week UX fix (no new models) that lifted the metric 58%, doubled downstream engagement, and halved cycle time. It became the company's north star.

Instrumentation A/B Experimentation Decision Quality
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North-star metric
Baseline 100
After Marquee 158

More Work

Marketing Attribution Rebuild

Roughly $7M in annual program spend ran against pipeline nobody could trace. I connected spend to CRM in the warehouse, diagnosed 30% of pipeline as unattributed, and rebuilt the UTM taxonomy and lead-source capture behind it. Multi-touch attribution then showed last-touch over-crediting bottom-of-funnel channels. Marketing reallocated, and blended CAC fell 15% over two quarters.

Attribution Marketing Measurement Data Contracts

Investment Qualification Platform

AWS needed a defensible way to choose which startups to back with credits. I owned the scoring framework behind that call, integrating PitchBook, Crunchbase, CBInsights, and Dealroom to score funding, GTM signals, and market fit. Scoring and segmentation models raised program efficiency 30%.

Product Strategy GTM Signals Analytics

Billing & Revenue Infrastructure

Revenue recognition ran on manual, error-prone effort. I led an internal billing automation tool, built on HubSpot, AWS Glue, and Mode, that generates invoices end to end and cut manual work 75%. When it moved onto a third-party metering platform, I was the product manager: conveying requirements to the vendor and owning testing. It now supports $32M in annual revenue.

Revenue Ops Vendor Management Product Management

Semantic Layer & Self-Serve Analytics

Teams pulled conflicting numbers and flooded analysts with ad-hoc requests. I standardized 40+ KPIs across Salesforce, billing, and external data into one semantic layer of business-ready entities anyone can self-serve, cutting ad-hoc reporting 35%.

Self-Serve KPI Strategy Enablement

Campaign Efficiency Measurement

Three organizations at AWS measured campaign performance three different ways. I owned the Redshift KPI layer over Finance spend, program data, and Adobe Analytics, and designed the conformed program dimension and identity bridge behind full-funnel measurement from first web hit to closed deal. It became the source of record for recurring business reviews across five orgs.

Full-Funnel Measurement Identity Resolution Alignment

Startup360 Analytics Platform

A customer-intelligence and seller-productivity product on Redshift, S3, and QuickSight that became the most-used seller tool in the AWS BI suite. Self-serve datasets reached 550+ users, seller productivity rose 16% year over year, and the platform drove 40% of monthly pipeline creation.

0→1 Product Self-Serve Seller Productivity

Experience

Supio

Sep 2024 – Present

Data Products

Founding data hire at an early-stage AI SaaS company. Built the analytics function, self-serve tooling, and measurement frameworks from scratch, then used them to shape the product roadmap.

  • Designed and shipped an AI-native agentic analytics platform, with dbt marts and a governed semantic layer exposed to Claude through an MCP server. Governed metrics answer first and novel questions route through human-in-the-loop validation. Time-to-insight fell from a 24-hour SLA to under 5 minutes and two analyst hires per function were avoided.
  • Shipped an AI-powered PM tool on the Claude API over Gong, Slack, Notion, and Intercom, used daily by every PM. Customer-research time fell roughly 60% and PRD drafting went from hours to minutes.
  • Connected roughly $7M in annual program spend to CRM pipeline, diagnosed 30% of pipeline as unattributed, and rebuilt the UTM taxonomy and multi-touch attribution model behind it. Blended CAC fell 15% over the following two quarters.
  • Owned measurement for the human-in-the-loop annotation pipeline, instrumenting operational signals to per-task effort and cost, and building integrity checks that flagged overstated vendor volumes. Workflow automations cut processing time 30%.
  • Built billing and revenue infrastructure on HubSpot, AWS Glue, and Mode, cutting manual invoicing effort 75%, then owned the metering-platform migration as PM. The system supports $32M in annual recurring revenue.
  • Designed the Fullstory and PostHog instrumentation that exposed the missing metric tying product usage to customer revenue, then shipped a 2-week UX experiment that lifted it 58%, doubled downstream engagement, and became the company north star.

Amazon Web Services

Jan 2021 – Jun 2024

Analytics & Insights Products

Promoted to lead product analytics for the AWS startup program, owning data products that guided credit, discount, and GTM decisions.

  • Built a startup-scoring platform on PitchBook, Crunchbase, CB Insights, and Dealroom whose scoring and segmentation models qualified startups for credit and discount programs and raised program efficiency 30%.
  • Built and governed a centralized semantic layer standardizing 40+ KPIs across Salesforce, billing, and external data, running a formal sign-off process that unified contested definitions into a single trusted source. Ad-hoc reporting fell 35%.
  • Owned campaign efficiency reporting for AWS Startups, designing the conformed program dimension and identity bridge that enabled full-funnel measurement from first web hit to closed deal.
  • Shipped Startup360 on Redshift, S3, and QuickSight, the most-used seller tool in the AWS BI suite, reaching 550+ self-serve users and driving 40% of monthly pipeline creation.

Amazon Web Services

May 2020 – Jan 2021

Business Intelligence

Joined AWS supporting Revenue and Sales Operations with data pipelines, dashboards, and go-to-market analytics.

  • Resolved a three-way definition dispute over how misrouted revenue should be measured, drove alignment through a 6-pager and SVP sign-off, then shipped the revenue-routing strategy on Redshift, Python, and Tableau that surfaced $82M in unmanaged revenue.

Perficient Inc.

Aug 2019 – Apr 2020

Data Engineering

Built and optimized healthcare data pipelines and dimensional models in a compliance-driven consulting environment.

  • Redesigned healthcare claims models with Kimball dimensional techniques, cutting processing errors 25% and securing compliance approval from key stakeholders.
  • Optimized SQL, Python, and shell pipelines, improving operational efficiency 20% and supporting a 15% improvement in clinical decision-making.

ADL Group

Sep 2016 – Jul 2017

Business Intelligence

Early analytics and BI role spanning SQL optimization and dashboarding for a real-estate data business.

  • Optimized T-SQL stored procedures and queries, improving query performance 30%.
  • Built Tableau dashboards tracking building-occupancy and KPI trends that helped drive 20% customer-acquisition growth.

Skills

Product & Measurement

Product roadmapping & discovery PRDs & 6-pagers KPI & metric definition A/B testing & experimentation Funnel, cohort & retention analysis Stakeholder alignment Build vs. buy & vendor management Operating cadence (WBR/QBR) Multi-touch attribution UTM taxonomy & lead-source capture

AI

Claude Code Claude API agents in production MCP server development Agentic workflow automation Prompt & context engineering Conversational analytics Human-in-the-loop validation design Cursor

Data Engineering

SQL Python PySpark Shell scripting (Unix, Bash) dbt Airflow AWS Glue ETL/ELT Dimensional modeling (Kimball) Data contracts & validation Monitoring & reconciliation Anomaly detection RLS & RBAC Git & PR review

Warehouses, Cloud & BI

Redshift BigQuery Databricks DuckDB PostgreSQL MySQL SQL Server (T-SQL) Snowflake AWS GCP Azure Tableau Looker QuickSight Power BI Mode Hex Sigma Excel

Instrumentation & GTM Systems

Fullstory PostHog Google Analytics Adobe Analytics Google Ads HubSpot Salesforce Label Studio Airtable

Education

Syracuse University

M.S., Information Management · GPA 3.79 / 4.0

2017 – 2019

University of Mumbai

B.E., Computer Engineering · GPA 3.75 / 4.0

2013 – 2016