Krishna Goje | Staff Data Platform Engineer

Signature Impact

Money Saved. Fraud Stopped. Speed Tripled.

Not just code shipped — business outcomes delivered. Each project represents a measurable impact on the bottom line.

$5M

Ernie AI VoiceBot

Architected the data pipeline powering Opendoor's AI sales agent (Ernie). The bot automates lead outreach with personalized market insights, targeting the 80% of seller leads that human agents never reach — projected to save $5M annually in operational costs.

@ Opendoor

$100M

Fraud Prevention Engine

Built the feature engineering pipeline for the Next-Gen Membership Rewards Fraud Identification system at American Express. Analyzed customer demographics and behavioral patterns across 40+ data sources, contributing to a system that prevented $100M in annual fraud losses.

@ American Express

60%

BI Platform Consolidation

Consolidated 100+ QuickSight and Snowflake datasets into 3 unified data models. Slashed compute and storage costs by 60%, saving ~$300K annually while improving data accessibility.

@ Opendoor

300%

Spark ETL Optimization

Managed a massive pipeline of 40+ data sources and 2000+ variables. Optimized processing times by 3x, consistently meeting strict SLOs for AmEx's data infrastructure.

@ American Express

NLQ

Self-Service Analytics Platform

Architected Opendoor's unified data environment with Snowflake Cortex + QuickSight Q. Enabled natural language querying across the entire property lifecycle — acquisition to resale.

@ Opendoor

ULD

Universal Listing Directory

Led the aggregation engine pulling restaurant data from Google, Uber Eats, and DoorDash. Empowered Grubhub's sales teams to onboard restaurants at scale and target incentives.

@ Grubhub

Career Journey

15 Years of Building

From engineer to team leader — a journey across continents, companies, and ever-growing scale and responsibility.

Aug 2024 — Present

Staff Data Platform Engineer (Sr. Manager-band)

Opendoor

Hyderabad, India

Technical lead — set platform standards, mentored engineers across dbt, Snowflake & BI
Architecting operations data platform across property lifecycle
Architected data pipeline for Ernie AI VoiceBot ($5M projected savings)
Consolidated 100+ datasets → 3 models (60% cost cut)

Jan 2023 — Aug 2024

Lead Data Engineer (Manager-band)

Booking.com

Hyderabad, India

Led engineering team migrating 15+ financial reporting pipelines to cloud-native architecture
Delivered profitability analytics used by finance leadership for multi-million dollar decisions

Nov 2021 — Dec 2022

Lead Data Engineer

Grubhub

Manhattan, NY

Led cross-functional engineering team building the Universal Listing Directory
Aggregated data from Google, Uber Eats, DoorDash → 300K+ restaurant partners

Jan 2017 — Nov 2021

Lead Data Engineer (Manager-band)

American Express

Manhattan, NY

Led & mentored team of 8–10 engineers — hiring, architecture, delivery
$100M fraud prevention engine (40+ sources, 2000+ variables)
300% Spark ETL speed boost; solved industry-wide small file problem
Hackathon Finalist & Data Geek award winner

Apr 2015 — Jan 2017

Data Engineer

American Express

Manhattan, NY

Built Real-Time Federated Query Engine for NoSQL
Reduced dev time & optimized query performance

Aug 2013 — Dec 2014

Software Engineer

East Texas A&M University

USA

Optimized university digital infrastructure

Current Role Deep Dive

18 Months at Opendoor

Joined Aug 2024. Single-handedly built the operations data infrastructure powering 70%+ of operational dashboards that leadership relies on daily.

Tickets in 10 Weeks

Dashboards Built

Data Models Rebuilt

Leadership Awards

Manual Spreadsheets Eliminated

15+

Stakeholders Served

6+ Executive Dashboards

Built and enhanced Weekly Operations Review, Sales Performance, Home Lifecycle, Property Assessments, Market Capacity, and C-Suite Executive Summary. Consolidated 3 fragmented reporting tools into unified views. The entire operations leadership's daily decisions run on these dashboards.

Ernie VoiceBot Data Pipeline

Built the data pipeline powering Opendoor's AI sales agent — personally credited by leadership in the company-wide launch announcement. The bot targets the 80% of seller leads that human agents never reach, generating personalized market insights for every call.

End-to-End Operations Data Platform

Architected a unified data platform spanning the entire property lifecycle — from initial home assessment through acquisition, renovation, listing, and final sale. Built an AI-powered executive summary layer on top that auto-generates a 10-line brief for C-suite, surfacing wins, risks, and action items across every stage. Also contributes to accountable.opendoor.com — the company's public investor transparency dashboard.

8+ Data Model Overhauls

Overhauled core data models across the acquisition, inventory, sales, and reporting layers. Fixed critical data quality bugs (800+ misclassified records), added financial settlement tracking, renovation type analytics, and mobile app integration. Part of a 17-dashboard, 5-domain data ecosystem.

Recognition from Leadership

🏆

Sr. Director of Operations — Formal company recognition award + public Slack praise: "Another huge win. Thank you."

🏆

Director of Home Operations — Formal recognition award for rapid dashboard delivery and turnaround speed

💪

Director of Data (skip-level manager) — Granted full authority to proactively deprecate outdated metrics: "Just nuke it. Don't ask." — a rare trust signal

⭐

Operations Analytics Leads — Repeated praise across channels: "Amazing, thank you!" & "Thanks for digging into this... Very helpful."

What People Say

Trusted by Leaders & Engineers

Recommendations from Directors, Staff Engineers, and colleagues across Fortune 500 companies and Big Tech.

A talented developer and engineer with a strong focus on efficiency and optimization — he was a reliable ally throughout numerous complex and technical development workstreams. I always appreciated his kindness and willingness to give his time to others, whether to help with personal technology queries or teach rooms full of people about Spark optimization.

Matthew K. Meyer

Director, Enterprise Fuel

American Express

Krishna is a fantastic person to work with. He is incredibly smart, kind, and has a great intuition. Krishna is great with large datasets and is able to simplify complex problems. I would highly recommend him for your team!

Sarah Mackey

Director of Product, Business Mobile Experiences

American Express

One of the engineers who shows the utmost dedication at what he does. He has in-depth technical and programming knowledge. He is adept at code optimizations and building reusable software components. Very organized and always focuses on the impact his work brings.

Yash Tekena

Data Engineering

Meta (prev. American Express)

Very knowledgeable and hardworking. Not only he completed his work on time, but he also helped in tracking any technical issues which may arise in production. Well versed in Big Data technologies and has excellent problem-solving skills. I would recommend him for any position.

Nikhil Sagar Mekhala

Data Engineer II

Amazon (prev. American Express)

View more on LinkedIn

AI-Powered Engineer

One Engineer.
The Output of a Team.

I engineered an AI operating system around my work that learns from every session, orchestrates 12 platforms, and compresses week-long sprints into hours.

Before — Traditional Workflow

⏳ Write SQL manually for each market

⏳ Run queries one at a time

⏳ Click through QuickSight UI for updates

⏳ Manually check dashboards for errors

⏳ Context-switch between 12+ tools

⏳ Repeat mistakes across sessions

~7 days per feature

→

After — AI-Augmented Workflow

⚡ AI generates SQL from natural language

⚡ 3 agents × 8 queries run in parallel

⚡ Python library updates dashboards programmatically

⚡ Auto-triggered data quality validators

⚡ One interface orchestrates all 12+ platforms

⚡ Self-learning system never repeats mistakes

< 1 hour per feature

7d→1h

Week to Hours

Custom AI Skills

12+

Platform Integrations

30+

AI-Managed Projects

4,874

Lines in QS Library

Self-Learning Hooks

krishna@opendoor ~ claude-code

# 1. Data investigation: 3 parallel agents × 8 queries each
$ claude "Why are Phoenix contracts 20% lower this week?"
→ Auto-triggered: data-investigate skill
→ Agent 1: Market volume (PHX, ATL, DAL, HOU, TPA, CLT, RLG, ORL)
→ Agent 2: Funnel drop-off tracing (PHX vs 4-week avg)
→ Agent 3: Upstream pipeline + Airflow DAG health
→ Root cause found in 45s: Pricing model update rejected 34% of PHX offers
# Previously: 2+ hours of manual SQL, tab-switching, Slack searching

# 2. Dashboard update: AI builds the visual end-to-end
$ claude /quicksight "Add renovation cost variance by market to weekly review"
→ Loading automation library (146 functions)... Backing up dashboard...
→ Writing SQL → Testing in data warehouse → Updating dataset → Adding visual
→ Validating data refresh... Verifying visual rendered correctly...
→ Done. Dashboard live with new metric in 90 seconds.

# 3. Slack Data Bot: Auto-answers stakeholder questions
$ claude /slack-data-bot "Check my mentions"
→ Found 3 @mentions from stakeholders in #ops-data
→ Investigating Q1: "What's our close rate by market this week?"
→ Running parallel Snowflake queries... Writer drafting response...
→ Reviewer checking accuracy... Iterating for clarity...
→ Polished response ready for copy-paste. L1 support automated.

# 4. Self-learning: every session makes the next one faster
$ claude /save-session
→ Captured: 3 anti-patterns, 1 speedrun optimization, 2 new error recoveries
→ AI config auto-updated. Next session starts smarter.

⚡ The AI Operating System I Architected

A custom-built ecosystem where AI sits at the center, orchestrating 76 specialized skills, 12+ platform integrations, and a self-learning feedback loop — all tuned to the company's business domain, data schemas, and team workflows.

Claude Code (Opus 4.6)

→

76 Auto-Triggered Skills

→

Parallel Agent Swarms

→

Business Outcomes

Snowflake (Data Warehouse)

QuickSight (BI Platform)

Databricks (ML/Analytics)

dbt (Data Modeling)

Airflow (Orchestration)

Slack MCP

Glean MCP

GitHub MCP

Linear MCP

Select Star

Google Workspace

Gmail

Self-Learning Hooks

→

Anti-Pattern Memory

→

Session Learnings

→

Auto-Config Evolution

Semantic Memory MCP

↔

Persistent Context Beyond Session Limits

↔

Infinite Knowledge Accumulation

What This Enables

I'm no longer just an analytics engineer. I'm an all-in-one AI-powered engineer who builds bots, ships dashboards, automates pipelines, and drives insights — all through a unified AI interface.

💬 L1 Support Bot (Slack Data Bot)

Built an AI agent that monitors Slack @mentions, auto-investigates data questions using parallel Snowflake queries, and drafts polished responses via a Writer/Reviewer iteration loop. Acts as L1 data support on behalf of stakeholders — answering questions that used to require an analyst's full attention.

📊 AI-Powered Executive Summaries

Developed Snowflake Cortex-powered natural language summaries embedded directly in QuickSight dashboards. Executives read a 10-line AI-generated brief and instantly understand how the operations landscape is performing — caveats, wins, and action items included. No more scrolling through 50 charts.

💻 Dashboard Automation Engine

Built a 4,874-line Python library with 146 functions for programmatic dashboard management. Dashboards are now built through AI: SQL generation, dataset updates, visual creation, data refresh — all with auto-backup and destructive change protection.

🧠 Self-Learning System

Every work session feeds learnings back into the system. It captures mistakes to avoid, winning patterns, and domain knowledge automatically. The AI starts each new session smarter than the last — compounding in speed and accuracy every week.

"Every engineer will use AI tools. I already engineered the system that learns from its own mistakes, remembers what worked, and compounds in speed every single week. The gap between this and a traditional workflow isn't incremental — it's a different category entirely."

76 custom skills · 12+ platform integrations · Self-learning feedback loops · Parallel agent swarms · L1 support automation · AI-generated executive insights

Get in Touch

Giving Back

Open Source

Production-born tools extracted from real enterprise workflows and open-sourced for the community.

⚙

quicksight-mcp

MCP Server · Python · MIT License

The most comprehensive AWS QuickSight MCP server — 31 tools for dataset management, analysis editing, visual creation, SPICE orchestration, and dashboard publishing. Built-in self-learning that tracks error patterns and SPICE timing.

31 Tools 4,500+ Lines 59 Tests Self-Learning

Born from managing 6+ executive dashboards at Opendoor. Extracted from a 4,874-line production library used daily.

View on GitHub →

💬

slack-data-bot

MCP Server · Python · MIT License

Autonomous data Q&A bot that monitors Slack for data questions, investigates using parallel AI agents across Snowflake, dbt, and metadata tools, then drafts polished responses via a Writer/Reviewer quality loop. Human-in-the-loop — never auto-sends.

8-Strategy Search Parallel Agents Writer/Reviewer Human-in-Loop

Born from handling 50+ weekly data questions at Opendoor. The bot drafts responses that sound like they came from the engineer, not AI.

View on GitHub →

These tools were built for production. So was I.

Krishna Goje
One Engineer.
The Output of a Team.

Money Saved. Fraud Stopped. Speed Tripled.

15 Years of Building

What I Build With

Certified & Verified

18 Months at Opendoor

Recognition from Leadership

Trusted by Leaders & Engineers

One Engineer.
The Output of a Team.

What This Enables

Open Source

Education

Let's Talk About What You're Building

Krishna Goje One Engineer.The Output of a Team.

Money Saved. Fraud Stopped. Speed Tripled.

15 Years of Building

What I Build With

Certified & Verified

18 Months at Opendoor

Recognition from Leadership

Trusted by Leaders & Engineers

One Engineer.The Output of a Team.

What This Enables

Open Source

Education

Let's Talk About What You're Building

Krishna Goje
One Engineer.
The Output of a Team.

One Engineer.
The Output of a Team.