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Technical Product Manager
Data Infrastructure
Updated on 3/15/2023
Experience Level
Desired Skills
Data Analysis
UI/UX Design
  • BS/MS/PhD degree in a technical field or equivalent practical experience building/shipping technical products
  • Experience developing/launching products/technologies in the data infrastructure space
  • Experience driving product vision, go-to-market strategy, and design discussions
  • Experience creating product roadmaps, and working with cross-functional teams
  • Ability to make things happen in a fast-paced, dynamic environment
  • Strong communication skills, and attention to detail
  • Understand the data analytics/ML ecosystem, markets, competition, and user requirements in depth
  • Understand data infrastructure technologies and tools from developer and enterprise perspectives
  • Deep understanding of what features data engineers and data scientists need, define long-term product strategy by proactively identifying emerging customer needs and business opportunities
  • Understand customer needs and how the Bodo platform can address them to guide roadmap prioritization decisions
  • Work very closely with engineers to design features, from architecture down to small UI/SDK details
  • Launch new products and features, test their performance and iterate quickly
  • Work closely with our initial users and make them successful by identifying key product gaps and potential improvements
  • Own product roadmaps and guide end-to-end product development

11-50 employees

Parallel computing for data analytics
Company Overview
Bodo helps bring supercomputing-style performance and scalability to developers and data scientists working on large-scale problems, helping bring new solutions to production in record time.
Company Core Values
  • We exist to change the face of analytics computing; to accelerate access to performance, scale, and simplicity for data-intensive applications.
  • Parallel computing is the only way to extend Moore’s Law for compute performance.
  • Data analytics developers work under a massive simplicity vs. performance gap. Parallel computing must be simplified/democratized for their use
  • Existing high-performance programming workarounds - like libraries and frameworks - fail to address the underlying compute scaling issues.
  • Python must be considered a “first-class” default platform