Data Engineer at Suno
Los Angeles, California, USA -
Full Time


Start Date

Immediate

Expiry Date

04 May, 25

Salary

240000.0

Posted On

04 Feb, 25

Experience

5 year(s) or above

Remote Job

No

Telecommute

No

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

ABOUT SUNO

At Suno, we are building a future where anyone can make music. You can make a song for any moment with just a few short words. Award-winning artists use Suno, but our core user base consists of everyday people making music — often for the first time.
We are a team of musicians and AI experts, including alumni from Spotify, TikTok, Meta and Kensho. We like to ship code, make music and drink coffee. Our company culture celebrates music and experimenting with sound — from lunchroom conversations to the studio in our office.

Responsibilities

ABOUT THE ROLE

We’re seeking talented data engineers to join our founding team, working closely with key stakeholders and taking ownership of building and shaping Suno’s core data foundation.
Check out our Suno version of the job here!

WHAT YOU’LL DO

  • Serve as a critical contributor to the Suno products team, providing insights and solutions that influence high-level decisions and shape the product roadmap.
  • Design, develop, and maintain complex data products, systems, platforms, and pipelines to build scalable, secure, and high-quality big data solutions that seamlessly integrate diverse data sources, process high-volume real-time and batch data, and ensure data integrity, quality, and compliance.
  • Collaborate with scientists, ML engineers, software developers, business leaders, and product teams to define data requirements, architect solutions, and implement data-driven opportunities that enhance customer experiences.
  • Leverage advanced analytics and data engineering practices to uncover actionable customer insights that drive the development of innovative and enhanced customer experiences for Suno.
  • Implement and monitor data systems to ensure high availability, reliability, and scalability while advocating for and applying best practices in big data engineering and analytics.
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