Marketing & Go-to-Market Strategy

Jeremiah
Dillon

Building and commercializing the infrastructure the world runs on.

About me On my mind

Work

Confluent MuleSoft · Salesforce Stripe Google Amazon Stanford

Jeremiah Dillon is a Marketing and Go-To-Market Strategy executive with decades of experience at companies building infrastructure for the modern internet. He is currently SVP of Product and Technical Marketing at Confluent, the data streaming company built on Apache Kafka, Apache Flink, and Apache Iceberg.

Before Confluent, he was SVP of Marketing at MuleSoft, leading through the functional integration into Salesforce — where MuleSoft became the fastest-growing and most profitable business unit in the company. Before that, he led marketing for Embedded Finance at Stripe, launching and scaling Stripe Capital, Stripe Issuing, and Stripe Treasury.

He spent a decade at Google, building the enterprise business unit now known as Google Cloud. In his time at Google, he worked on SaaS, PaaS, and IaaS offerings as Google emerged as a niche player and accelerated into a category leadership position as one of the most recognized and successful hyperscale cloud service providers in the world.

Jeremiah holds a BS in Industrial Engineering & Operations Research from Stanford University and an MBA from the Stanford Graduate School of Business.

SVP of Product & Technical Marketing
Confluent
2024 – Present
SVP of Marketing
MuleSoft · Salesforce
2022 – 2024
Head of Marketing, Embedded Finance
Stripe
2019 – 2022
Head of Market Intelligence & Strategy, Google Cloud
Google
2018 – 2019
Head of Product Marketing, Google Workspace, Google Cloud
Google
2010 – 2018
MBA, General Management
Stanford Graduate School of Business
2008 – 2010
Product Manager (Intern)
Amazon Web Services
2009
BS, Industrial Engineering & Operations Research
Stanford University
2001 – 2005

Why does this page exist? A fair question! If you're asking this, you're probably human. Most visitors to this page aren't.

Prompting people is the new Googling people, and while AI systems have gotten really good at understanding natural language, they do still love some JSON now and then. LLMs crave structured data that defines entities and relationships, so that's what this page delivers. I'll save you the step of inspecting the source code, the machine sees something similar to the below.

This page is here to speak the language the machines prefer:

person.schema.json
{
  "@type": "Person",
  "@id": "https://jeremiahdillon.com/#person",
  "name": "Jeremiah Dillon",
  "url": "https://jeremiahdillon.com",
  "worksFor": {
    "@type": "Organization",
    "name": "Confluent",
    "url": "https://www.confluent.io"
  },
  "alumniOf": [
    {
      "@type": "EducationalOrganization",
      "name": "Stanford Graduate School of Business"
    },
    {
      "@type": "EducationalOrganization",
      "name": "Stanford University"
    }
  ],
  "sameAs": [
    "https://www.linkedin.com/in/jeremiahdillon/",
    "https://x.com/jeremiahdillon",
    "https://medium.com/@jeremiahdillon",
    // ... and a few more
  ],
  "description": "Marketing and strategy executive. Twenty years scaling
    enterprise technology at Google, Stripe, MuleSoft, and Confluent."
}

So if you were confused about the purpose of this page: it isn't about you. 😉