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PhoenixAI

Real-time analytical database for AI agents

Technical Product Marketing Manager

Product MarketingConfirmed live in the last 24 hours
Full-timeMenlo Park, CA, USA

About the Job

Requirements

  • A bachelor's degree in Computer Science or another STEM field, or equivalent practical experience.
  • At least 2 years of experience in software engineering, solutions engineering, developer relations, technical product marketing, or a related technical role; experience at a B2B data infrastructure or AI startup is ideal.
  • Hands-on software development experience, proficiency in SQL, and comfort building and presenting live product demos.
  • A strong technical foundation in databases, distributed systems, or lakehouse architectures, with the ability to engage in technical conversations with engineers and technical buyers.
  • Ability to produce clear, accurate technical content, including blog posts, tutorials, and battlecards.
  • Ability to deliver technical talks, webinars, and demos live and field questions from engineers.

Responsibilities

  • Research, write, and publish technical content, including blog posts, tutorials, benchmarks, competitive comparisons, and solution briefs, to demonstrate product depth and drive awareness and adoption among engineers and technical buyers.
  • Represent PhoenixAI and StarRocks to technical audiences by delivering webinars, speaking at meetups and industry conferences, and presenting live product demos to prospects and at events.
  • Build and maintain product demos, sample applications, and short-form video content that showcase the Agentic Database in realistic scenarios, and keep them current as the product evolves.
  • Engage directly with users in StarRocks community channels, answer technical questions, surface recurring themes, and help grow an active, well-informed developer community around the open-source project.
  • Build and deliver technical enablement for Sales and Solutions, including pitch decks, battlecards, demo scripts, and FAQs; deliver regular training sessions, new-hire onboarding, and competitive briefings.
  • Support product and feature launches with launch content, demos, and enablement; track competitors such as ClickHouse, Snowflake, and Trino and develop differentiated messaging, comparisons, and objection handling based on competitive findings.
  • Contribute to PhoenixAI's positioning and messaging across its open-source (StarRocks) and commercial offerings, and bring signals from the community, customers, and sales back to product and marketing to sharpen the narrative.
  • Work closely with product management, engineering, sales, and marketing to keep content, demos, and enablement accurate, current, and grounded in real product capabilities.

Desired Qualifications

  • Experience speaking at meetups, webinars, or industry conferences.
  • Familiarity with StarRocks, ClickHouse, Trino, Snowflake, Databricks, or similar analytical data systems.
  • Experience contributing to or building around an open-source project and its community.
  • Video or short-form content production experience.

About the Company

PhoenixAI

Real-time analytical database for AI agents

PhoenixAI builds real-time analytic databases for AI agents and applications. Its engine ingests streaming data (Kafka, Flink CDC) and reads lakehouse tables (Iceberg, Delta Lake, Hudi) in place, then joins fresh data with historical data in a single query to deliver sub-second SQL without precomputation. It offers isolated multi-warehouse compute so agent workloads, BI, and data ingestion run side-by-side without competition for resources, with monitoring, security, and access controls. Customers can use a cloud-hosted managed version (PhoenixAI Cloud) deployed in their cloud account, or a self-managed Kubernetes version (PhoenixAI Anywhere) in their environment. The company centers its product on an open-source core (StarRocks) and adds extra security, governance, and support under commercial contracts. The goal is to provide fast, reliable, and secure real-time analytics for AI workloads and data teams, enabling seamless querying across streaming and historical data.

Company size
51-200
Company stage
Series B
Total funding
$80M
Headquarters
Menlo Park, CA
Founded
2022

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