JADY TIAN

Senior Technical Product Manager

Jady Tian

Building enterprise data platforms, APIs, and AI-ready products.

Software engineer turned product manager, leading platform products at Capital One and BlackRock. I'm currently unifying Capital One's fragmented payment systems into a single AI-ready enterprise data platform; before that I built reporting products supporting $322B in assets. I enjoy taking complex technical systems and making them simple, scalable, and useful.

Focus
Data platforms · APIs · AI-ready products
Currently
Senior Product Manager, Capital One Enterprise Payments
Background
Software Engineering · Product Management · Operations Research
↓ Featured work

01 · Featured Work

Capital One

Enterprise Payments Data Platform

Canonical Data · GraphQL APIs · AI Readiness

Unifying 7+ fragmented payment systems into a single golden record for every payment: one canonical model, one GraphQL interface, and an AI-ready data foundation.

View case study →

BlackRock

Whole Portfolio Reporting Platform & APIs

Snowflake · Aladdin Data Cloud · Reporting APIs

Unified fragmented private markets reporting into a cloud-native platform supporting $322B in assets, and turned static reports into reusable API products for enterprise clients.

View case study →

Capital One

Marketing Intelligence Platform

Analytics · Measurement · Data Products

Consolidated fragmented marketing data into standardized datasets and dashboards, giving marketing teams consistent campaign measurement and self-service analytics.

View case study →

PurryMe · Marketplace Product

Consumer Mobile App

Marketplace · Mobile · 0 → 1

Designed an end-to-end marketplace helping cat owners discover trusted sitters through streamlined booking, messaging, and real-time updates.

View case study →

02 · About

I'm a product manager with a software engineering background who enjoys building highly technical products that simplify complex systems.

Over the past several years I've led platform initiatives spanning enterprise payments, reporting infrastructure, APIs, data products, and analytics, from modernizing reporting infrastructure at BlackRock to unifying enterprise payment data at Capital One.

Outside work I enjoy building consumer MVPs, photographing cities, and experimenting with AI-powered products. I volunteer teaching kids art and coding and at a local animal shelter, and unwind with yoga and tennis.

Why the engineering background matters: I can read the code, sit in the architecture review, and still keep the conversation anchored to the customer outcome. That's the job of a technical PM.

03 · Experience

Work Experience

2025 to Present
Senior Product ManagerEnterprise Payments
Capital One
2022 to 2025
Product ManagerPrivate Markets Data Platform
BlackRock
2021 to 2022
Software EngineerInvestment Platform
BlackRock

Education

2017 to 2021
Columbia UniversitySchool of Engineering and Applied Sciences
B.S. Computer Science & Operations Research

04 · Skills

Product

Product Strategy Roadmapping Product Discovery Platform Products Enterprise SaaS B2B Products User Research Experimentation MVP Development AI Product Strategy

Technical

Technical Product Management API Products REST APIs GraphQL SQL Python Java JavaScript

Data

Data Products Snowflake Databricks dbt ETL Analytics Reporting Platforms

Ways of Working

Agile Stakeholder Management Cross-functional Leadership

05 · How I Think

How I prioritize features

Sequence by what unlocks the most downstream value, not by what's loudest. On platform products that usually means the unglamorous capability that three other teams are quietly blocked on.

How I write PRDs

A PRD is a decision record, not a novel. Problem, constraints, the options considered, and why we chose this one, written so an engineer who joins in six months understands the "why" without a meeting.

How I work with engineers

I was one. I bring engineers into discovery early, surface assumptions before implementation begins, and never hand over requirements I couldn't defend in an architecture review.

How I approach platform products

A platform's customers are other product teams. Treat internal consumers with the same discovery rigor as external users: interview them, map their integration journeys, and measure adoption like revenue.

What good APIs look like

Predictable, well-documented, and boring in the best way. Consistent resource naming, explicit error contracts, versioning strategy decided before v1 ships, because migration pain compounds.

How I use AI in product management

AI accelerates drafts like PRDs, synthesis, and prototypes, but judgment stays human. I also build with it directly: prototyping AI-powered consumer MVPs keeps my intuition for the technology honest.

How I define product success

One north-star outcome per product, instrumented from day one. For platforms: adoption by downstream teams and reduction in their effort, like the 60% reporting-effort reduction we measured at BlackRock.

The thread through all of it

Verifiable beats opinionated. Show the data, show the reasoning, show the trade-off, whether it's a nutrient formula, a payment API, or a $322B reporting platform.

← Back to all work

Case Study · Capital One

Enterprise Payments Data Platform

Enterprise Data Platform · Payments · GraphQL APIs · Event-Driven Architecture · Canonical Data Models · AI Readiness

Overview

Capital One's payment ecosystem spans multiple legacy platforms responsible for ACH, wire, and internal payment processing. Because each system evolved independently, operational teams often had to manually reconcile payment events across multiple databases to answer even basic payment-status questions.

As the product manager for the Bank Payments Data Product, I lead product strategy for an enterprise platform that unifies these fragmented systems into a single canonical data layer that enables scalable APIs, analytics, operational tooling, and future AI-powered applications.

The Problem

Payment information was fragmented across 7+ independent systems, each with different schemas, identifiers, and event models. For the teams operating on top of them, that meant:

No single source of truth for payment lifecycle data Manual reconciliation across systems Inconsistent payment identifiers Duplicated downstream integrations Limited observability across processing Poor customer transparency into payment status

The cost of that fragmentation became concrete in a single production incident: isolating the underlying issue took 4 to 6 weeks of investigation spanning six business organizations.

$12.9B
Payment volume impacted
~3M
Customers affected
$56M
Gross loss before the issue was isolated

The customer side of the problem was just as clear: research identified payment visibility as the largest source of customer frustration, with 38% of wire complaints citing limited end-to-end payment transparency.

The Solution

A single Golden Record for every payment.

The Bank Payments Data Product consolidates fragmented payment systems into one enterprise data platform. Rather than integrating separately with each legacy system, downstream applications consume standardized payment data through a single enterprise GraphQL interface. The platform introduces:

  • A canonical payment data model
  • A stable Payment Lifecycle ID
  • Event-driven ingestion
  • Medallion Architecture
  • GraphQL APIs
  • Enterprise metadata governance
  • An AI-ready data foundation

My Role

I own product strategy and roadmap for the enterprise Payments Data Product, spanning canonical data modeling, GraphQL API strategy, requirements definition, data governance, AI readiness, and release planning. I partner closely with Engineering, Architecture, Operations, Compliance, Analytics, and Platform teams throughout discovery and delivery.

Key Product Decisions

Canonical data model

Defined a standardized enterprise payment model spanning 100+ payment attributes across ACH and wire processing, enabling consistent downstream consumption.

GraphQL API strategy

Led product strategy for a unified GraphQL layer that abstracts multiple legacy payment systems into a single developer experience.

Enterprise data governance

Established metadata standards, observability requirements, ontology alignment, and governance processes supporting enterprise-scale data quality.

AI readiness

Defined canonical schemas and metadata structures that prepare enterprise payment data for LLM-powered operational workflows and intelligent search.

Platform adoption

Worked directly with Operations, Compliance, Risk, and Analytics teams to prioritize capabilities that reduce manual investigations and improve operational efficiency.

Business Impact

The platform is in active delivery; these are the program objectives it is built to hit, with launch metrics to follow:

  • Consolidates 7+ payment platforms into a unified enterprise data product
  • Eliminates bespoke downstream integrations through standardized GraphQL APIs
  • Establishes a single Golden Record for every payment
  • Improves operational visibility across enterprise payment processing
  • Creates a scalable foundation for analytics, compliance, and AI-powered applications
← Back to all work

Case Study · BlackRock

Whole Portfolio Reporting Platform & APIs

Snowflake · Aladdin Data Cloud · Reporting APIs · Data Platform · Enterprise SaaS

Overview

Institutional investors relied on fragmented reporting systems that made analyzing private market investments slow and difficult. Our team modernized reporting by creating a cloud-native platform powered by Snowflake and Aladdin Data Cloud, transforming reporting from static exports into reusable platform capabilities, with API products exposing portfolio, accounting, investment, and performance data in a consistent way.

Problem

Institutional clients increasingly wanted to integrate data directly into their own reporting tools instead of relying on manually generated reports. Legacy systems stood in the way:

Multiple reporting systems Legacy infrastructure Manual reporting Inconsistent data definitions Duplicated reporting logic Limited integration options Slow analytics

Solution

Designed a scalable reporting platform supporting:

  • Reporting APIs
  • Standardized data models
  • Whole Portfolio reporting
  • Snowflake infrastructure
  • Enterprise analytics

My Contributions

Product vision

Defined the roadmap across reporting APIs, standardized datasets, and enterprise reporting capabilities.

Platform design

Partnered with engineering to create scalable Snowflake data models supporting positions, accounting, cash flows, and performance reporting.

Customer discovery

Worked directly with client-facing teams to prioritize reporting capabilities based on institutional investor needs.

Cross-functional leadership

Coordinated delivery across engineering, QA, operations, analytics, and platform teams.

API Products on Aladdin Data Cloud

As part of the broader Aladdin Data Cloud initiative, I led product work for a portfolio of reporting APIs that let enterprise clients and internal product teams access standardized investment data through modern API interfaces.

API product strategy

Defined which reporting capabilities should become reusable APIs rather than one-off reporting solutions.

Standardized data contracts

Worked with engineering to establish consistent response structures and reporting definitions across APIs.

Developer experience

Made APIs intuitive for downstream engineering teams through better documentation, consistency, and discoverability.

Roadmap prioritization

Balanced client feature requests against foundational platform investments that would benefit multiple products.

Impact

$322B+
Assets supported
60%
Reduction in reporting effort
9
Enterprise clients
450+
Stakeholders
3
Reporting platforms unified
50+
Requirements delivered
← Back to all work

Case Study · Capital One

Marketing Intelligence Platform

Marketing Analytics · Measurement · Attribution · Data Products · Dashboarding

Overview

Led product initiatives focused on marketing intelligence and campaign measurement, enabling marketing teams to better understand customer engagement and business performance through centralized analytics and reporting.

The platform consolidated marketing data into standardized datasets and dashboards, improving visibility across campaigns and supporting more data-driven decision making.

Problem

Marketing data was fragmented across multiple systems, and teams spent significant time manually combining data before they could answer basic questions:

  • Which campaigns perform best?
  • Where are customers converting?
  • Which channels drive engagement?
  • How should future marketing investments be prioritized?

Objectives

Centralize marketing reporting Improve measurement consistency Reduce manual reporting Enable self-service analytics Standardize KPIs Support executive reporting

My Contributions

I worked closely with marketing, analytics, and engineering teams to define reporting requirements, prioritize analytics capabilities, and drive roadmap decisions.

Metrics framework

Partnered with stakeholders to standardize marketing KPIs across campaigns.

Reporting experience

Defined reporting capabilities that let marketing teams quickly evaluate campaign performance.

Data quality

Worked with engineering to improve consistency and reliability of marketing datasets.

Stakeholder alignment

Balanced competing reporting requests from multiple business teams while maintaining a scalable roadmap.

Outcomes

Faster reporting cycles Standardized campaign measurement Reduced manual analysis Broader stakeholder adoption More data-driven marketing decisions
← Back to all work

Case Study · PurryMe

A Marketplace for Trusted Cat Sitters

Consumer Mobile · Marketplace · 0 → 1 Product

Problem

Finding trustworthy cat sitters is surprisingly difficult.

Existing platforms prioritize dogs, provide limited transparency, and require fragmented communication across apps, texts, and phone calls.

Discovery

  • Interviewed cat owners about past sitting experiences
  • Mapped end-to-end booking journeys
  • Identified trust as the biggest pain point
  • Prioritized communication and transparency in the MVP

Solution

Built a mobile marketplace supporting:

  • Onboarding
  • Sitter discovery
  • Messaging
  • Booking
  • Payments
  • Reviews

Product Decisions

Verified sitter profiles

Trust was the core pain point, so verification became the marketplace's foundation, not an afterthought.

Real-time updates

Photo and status updates during visits, replacing the anxious "how's my cat?" text thread.

Reusable pet profiles

Feeding routines, medications, and quirks entered once and shared with every sitter, so there are no repeated briefings.

Structured booking flow

A guided request → confirm → visit → review flow, so expectations are explicit on both sides.

MVP Roadmap

Phase 1

  • Booking
  • Messaging
  • Profiles

Phase 2

  • Ratings
  • Payments
  • Notifications

Phase 3

  • Subscriptions
  • AI recommendations
  • Recurring bookings