Platform · Commerce · Digital Product Manager

Venkata Sai Chandradeep Telaprolu

Senior Platform Product Manager with 6+ years of experience building scalable payments, data, and workflow automation platforms — spanning financial services, enterprise commerce, and retail digital products. Led cross-functional teams to deliver straight-through onboarding, checkout optimization, and enterprise-scale platform systems.

Platform PM Payments Enterprise & Commerce Platforms Workflow Automation AI Products

View Experience Get in Touch

180+

Clients Onboarded

Enterprise scale delivery across digital onboarding

Products Supported

Platform and product definitions enabled across workflows

90%

Failure Reduction

Submission failures reduced via upfront validation + STP

62%

Rework Reduction

Operational handoff and correction cycles reduced

Core Competency

Technical Footprint

API-first, metadata-driven platforms orchestrating intake, validation, and downstream automation across enterprise systems.

Architecture

API-first, metadata-driven platforms orchestrating intake, validation, and downstream automation across multiple systems.

Interfaces

Internal APIs, schema-based data contracts, and reusable workflow primitives enabling publish-once, reuse-everywhere execution.

Governance & Security

Role-based access control (RBAC), approval workflows, and definition lineage embedded directly into platform flows.

Scale & Reliability

180+ enterprise clients, 45+ sub-products, 48 product integrations and 15 back-office APIs supported with consistent SLAs.

AI + Automation

Productized AI Capabilities

Built to improve intake quality, authoring speed, and operational visibility.

Intake Quality Copilot

Improve submission quality before routing by catching missing fields and flagging inconsistencies.

Product Definition Copilot

Draft reusable definitions and flag downstream impacts for approval across the product catalog.

Experiment Planner

Propose hypotheses, variants, and instrumentation for checkout fixes and workflow improvements.

Ops Insights

Surface STP coverage, bottlenecks, and guardrail exceptions for operational teams in real time.

Governed Analytics Platforms

Governed analytics platforms enabling AI and automation with RBAC-secured data access.

Role-based AI Personalization

Role-based AI personalization embedded directly into platform workflows to reduce drop-offs.

Platform Strengths

Platform Product Management API Platform Strategy AI-Enabled Product Configuration Spec-Driven Development Data Platform Governance Workflow Automation Digital Payments Infrastructure

Career Overview

Experience

6+ years delivering platform and digital products across payments, data infrastructure, and workflow automation.

Apr 2024 — Present

J.P. Morgan Chase & Co.

USA

Platform Product Manager — Payments

Own fast-track onboarding for JPMorgan's Digital Banking platform (web + app), enabling activation for Wires, ACH, RTP, and Check Deposits across dependent APIs and back-office systems.

Key Outcomes

Drove straight-through onboarding by pre-populating required fields and adding upfront validation; reduced submission failures by 90% and rework by 62%
Cut onboarding cycle time from ~72 hours–1 week to under 1 hour by removing manual handoffs and standardizing workflow execution
Scaled adoption from 45 clients/15 products to 180 clients/45 sub-products; supported 48 products and 15 back-office API integrations
Designed and launched a Unified Product Intake platform consolidating discovery and requests across multiple digital platforms, including AI-powered role-based personalization
Built a self-serve, prompt-driven authoring tool for Product Owners; reduced onboarding tickets by 80% for the supported segment
Owned the Catalog/Product Definition layer as a single source of truth across onboarding systems to reduce duplicate builds and engineering spend

Platform Digital AI STP Automation Governance

Aug 2022 — Mar 2024

J.P. Morgan Chase & Co. – Product Sales

USA

Product Manager, Analytics Solutions

Owned the Payments sales data product end-to-end from CRM source systems through ingestion, modeling, and publishing in Amazon Redshift as a single reliable source for sales reporting.

Key Outcomes

Partnered with CRM Product Owners to understand sales objects and processes; defined metadata and data dictionaries and coordinated extraction into the data platform
Wrote and prioritized user stories for CRM and ingestion teams; collaborated with data engineering to model datasets and publish analytics-ready views
Embedded governance by design by working with data stewards on quality and lineage, and implementing RBAC/IAM and row-level security across Lines of Business
Monitored usage and adoption and guided teams to the right datasets to ensure the data product delivered measurable business impact
Managed the Jira backlog and continuously re-prioritized stories to deliver the highest-value analytics capabilities first

Data Platform Analytics Product Governance & Security AI Readiness Sales Enablement

Oct 2021 — Jun 2022

Yum! Brands (USA)

USA

Product Analyst (Associate Product Manager)

Owned the checkout funnel view (menu → cart → checkout → payment → confirmation) across web and mobile to surface drop-offs and guide payments improvements.

Key Outcomes

Analyzed declines, timeouts, and retries by device/browser/order type and gateway response; translated insights into engineering priorities
Partnered with engineering to improve tracking/instrumentation so release impact could be measured confidently
Supported A/B tests and pilots (error messaging, retries, saved payment, guest flow) and shared results to guide rollouts
Balanced customer experience with payments controls and reliability by aligning Product, Design, Ops, and Risk/Compliance

Digital Analytics Payments Experimentation

Jun 2020 — Oct 2021

Reguss Consulting (India)

India

Analytics Consultant

Built analytics platforms for marketing and sales teams, focusing on automation and KPI governance.

Key Outcomes

Built lead-scoring models and campaign dashboards to focus sales on high-propensity clients
Automated data pipelines using SQL, PySpark, and Airflow; reduced report turnaround time by 30%
Created KPI hierarchies and scorecards for marketing and sales performance tracking
Helped business leaders shift from manual reporting to automated analytics workflows

Automation Analytics Data Products

Jan 2019 — Jan 2020

Dell Technologies

India

Business Development Intern — Sales

Supported sales segmentation, territory planning, and forecasting with TAM/SAM sizing and competitive mapping.

Key Outcomes

Built weekly pipeline and forecast views (Excel/Power BI from CRM) to improve stage hygiene and forecast accuracy
Assisted win/loss reviews, tagging reasons and surfacing pricing/packaging patterns to inform sales plays
Created enablement assets (battlecards, one-pagers, ROI inputs) to sharpen messaging and speed deals

Go-to-Market Analytics

Education

Executive MBA

University of the Cumberlands

MS, Business Analytics

University of Louisville

B.Tech, Computer Science

Bharath University

Case Studies

Platform Product Case Studies

Structured problem → solution → architecture → impact narratives that show end-to-end platform product ownership.

01 / Case Study

Fast-Track Onboarding STP
(Digital Banking Platform)

Digital banking onboarding across web and mobile for startup and mid-market clients, covering Wires, ACH, RTP, and Check Deposits with back-office integrations.

Problem

Onboarding was slow and manual (72 hours–1 week) due to repeated handoffs, missing information, and inconsistent data capture, leading to high rework and support load.

Solution

Standardized onboarding workflow with upfront validation, pre-populated fields, guided data capture, and automated assignment/acceptance steps tied to back-office APIs.

Impact

Onboarding time reduced from 72 hours–1 week to under 1 hour
Submission failures reduced by 90% and rework lowered by 62%
Onboarding tickets reduced by 80% for the supported segment
Scaled adoption from 45 clients/15 products to 180 clients/45 sub-products
Improved release stability by reducing workflow breakages and cycle-time variance

Technical Focus: Workflow orchestration · Upfront validation · API-driven straight-through processing

Digital Banking Onboarding Platform — Layered View

Client Portal / Product Intake

AI Personalization Layer

Product Definition Catalog

Workflow Orchestration Engine

API Integration Layer

Payment Services & Back Office Systems

02 / Case Study

Checkout Funnel Optimization
Yum! Brands · 50M+ Digital Orders / Year

Consumer-facing digital ordering funnel (KFC, Pizza Hut, Taco Bell) spanning web and mobile apps, serving tens of millions of orders annually across multiple payment gateways and tender types.

Problem

Drop-offs, declines, and timeouts were hard to diagnose with limited instrumentation, making it impossible to isolate issues by device, browser, or gateway.

Solution

Instrumented funnel events, analyzed failure patterns by device/browser/gateway, and ran experiments on messaging and retry flows to improve completion rates.

Impact

Reduced checkout drop-off rate via A/B-tested messaging, retry flows, and gateway failure fixes — directly improving order conversion across 50M+ annual consumer transactions
Built real-time funnel instrumentation enabling same-day diagnosis of payment failures by device, browser, and gateway — reducing operational response time from days to hours
Expanded payment tender coverage (credit, debit, digital wallets) improving checkout success rates for a broader consumer base

Technical Focus: Consumer funnel instrumentation · A/B experimentation · Payment gateway failure analysis · Omnichannel checkout · Tender type expansion

Checkout Funnel — Failure Taxonomy

Menu → Add to Cart

Cart → Checkout Entry

Payment Selection + Auth

Gateway Processing

Order Confirmation

03 / Case Study

Payments Sales Analytics
Data Platform (CRM → Redshift)

Enterprise Payments Sales organization requiring trusted, reusable sales data across multiple Lines of Business.

Problem

Sales data fragmented across CRM objects with inconsistent definitions, manual reporting effort, and limited reuse for analytics and ML models.

Solution

Built a governed analytics data product by defining metadata, data dictionaries, ingestion requirements, and analytics-ready models in Amazon Redshift.

Impact