Platform And Product Engineer

Shashwat Srivastava

I build the systems that make product teams faster.

I work on the infrastructure behind product experiences, from GTM search, the customer API/MCP, and data systems at Nooks to data and ML platforms at Opendoor.

Platform systemsProduct-facing infrastructure
Explore the journey↓

Snapshot

Now

I’m building platform and data systems at Nooks.

Focus

GTM search, the customer API/MCP, and data infrastructure for natural-language analytics, alongside an agent context layer.

Background

I studied Electrical and Computer Engineering at Carnegie Mellon, with minors in Computer Science and Economics.

What I work with

I spend most of my time around data, product infrastructure, and cloud systems.

If you want the quick version before opening the chat, this is the stack I've spent the most time in.

Programming languages

I use these across backend services, data systems, and analytical work.

PythonJavaGoSQLTypeScript

Cloud and infrastructure

I’m comfortable across AWS, GCP, and the infrastructure that supports containerized systems and platform tooling.

AWSEC2S3IAMRDSDynamoDBGCPCloud SQLGoogle Kubernetes EnginePub/SubTerraformKubernetesDocker

Data and ML platforms

I’ve built and operated data platforms, feature stores, and model pipelines that move from research into production.

SparkAirflowMLflowDatabricksSnowflakeBigQueryFeature storesML servingTraining pipelines

Backend systems and frameworks

I’ve built both product-facing and platform-facing systems across services, monitoring, and web tooling.

FlaskDjangoSeleniumSynthetic monitoringComputer visiongRPCProtobuf

Career Journey

I've spent my career close to the systems that make products work.

Nooks · 2025-PresentOpendoor · 2018-2025AppDynamics · 2016-2018Carnegie Mellon · 2012-2016
What I'm doing now

Nooks

At Nooks, I work across product and platform engineering on GTM search, the customer API/MCP, data infrastructure, and a new context layer for agents.

2025-Present

What I was focused on

  • I joined as an Applied Machine Learning Engineer and quickly expanded into a mix of product and platform work.
  • I worked on database stabilization and a Postgres-to-Elastic shift, including the pipelines needed to unify GTM data.
  • I’m one of the original authors of the customer API/MCP, enabling customers to interact with Nooks agentically.
  • I shipped the first data replication project for a unified GTM search engine at much higher scale.
  • I set up Snowflake data warehouse infrastructure within our data platform, helping revamp the analytics platform to answer natural-language queries and advising on product use cases and how to use the warehouse.
  • I’m working on a context layer to provide the foundation agents run on and the context they use.
AI PlatformElasticPostgresCustomer API/MCPSnowflakeAgent Context

What I built

A few of the systems I worked on during this stretch.

Featured System

Unified GTM Search

01

I built the first data replication and search infrastructure for a unified GTM search engine, moving search-heavy workflows from Postgres to Elastic so it could stay fast and reliable as we scaled toward enterprise use cases.

Customer API/MCP

02

I’m one of the original authors of the customer API/MCP, enabling customers to interact with Nooks agentically.

Snowflake + Analytics Platform

03

I set up Snowflake data warehouse infrastructure within our data platform.

Agent Context Layer

04

I’m working on a context layer to provide the foundation agents run on and the context they use.

Data + ML platform

Opendoor

I spent several years across data infrastructure, backend systems, and ML platform work at Opendoor, building the foundations behind research, forecasting, and operations at scale.

2018-2025

What I was focused on

  • I started as the first engineer on a data acquisition platform for streaming and batch research data.
  • I led major migrations including Spark to Databricks and warehouse tooling onto Snowflake.
  • I built backend systems and datasets that became internal sources of truth for home data nationwide.
  • Later, I shifted into ML platform work across feature stores, model serving, and forecasting pipelines.
PythonData InfrastructureML InfrastructureFeature Store

What I built

A few of the systems I worked on during this stretch.

Data Acquisition Platform

01

I was the first engineer on the platform for ingesting and processing research data at scale.

Spark on Databricks

02

I led the migration to Databricks and helped drive shared Spark adoption across the company.

ML Platform + Forecasting

03

I worked on the feature store, model serving, and orchestration for forecasting pipelines and simulations.

Early backend work

AppDynamics

I worked on the globally distributed backend of a Selenium-based synthetic monitoring product, with a lot of focus on cost, efficiency, and performance.

2016-2018

What I was focused on

  • I worked on globally distributed Selenium jobs and the backend systems that supported synthetic monitoring.
  • I used data-driven analysis to improve throughput, reduce waste, and redesign critical paths around long-running services and parallel pipelines.
  • I learned early on how instrumentation and cost shape production systems.
SeleniumSynthetic MonitoringInstrumentationObservability

What I built

A few of the systems I worked on during this stretch.

Distributed Selenium Jobs

01

I worked on globally distributed browser monitoring workloads.

Cost + Performance Modeling

02

I used historical data and budget models to guide infrastructure choices.

Parallel Pipeline Redesign

03

I improved throughput, efficiency, and fleet utilization by redesigning critical paths.

How I got started

Carnegie Mellon

I built my engineering foundation at Carnegie Mellon in ECE, with minors in Computer Science and Economics.

2012-2016

What I was focused on

  • I completed a B.S. in Electrical and Computer Engineering with minors in Computer Science and Economics.
  • I built depth across systems, software construction, machine learning, and data structures.
  • I used Django, embedded systems, and real-time software projects to learn how to move from theory into working systems.
ECEComputer ScienceEconomicsSystems

What I built

A few of the systems I worked on during this stretch.

Systems + ML Coursework

01

I built depth in computer systems, machine learning, data structures, and software construction.

Product Projects

02

I worked on Django apps, embedded systems, and project-based engineering work.

Technical Range

03

This is where I built both analytical depth and practical instincts for shipping.