Warner Music Inc.

Senior Software Engineer, Dimensional Data

Ontario · Senior

Sponsorship not specifiedCAD 112k-CAD 152kDetected 36 days ago

TypeScriptPythonReactDistributed SystemsBackend DevelopmentFull-Stack DevelopmentPostgreSQLDatabricksAWSData EngineeringCollaboration

About the role

At Warner Music Group, we're a global collective of music makers and music lovers, tech innovators and inspired entrepreneurs, game-changing creatives and passionate team members. Here, we turn dreams into stardom and audiences into fans. We are guided by three core values that underpin everything we do across all our diverse businesses: • Curiosity: We do
our best work when we're immersing ourselves in culture and breaking through barriers. Curiosity is the driving force behind creativity and ingenuity. It fuels innovation, and innovation is the key to our future. • Collaboration: Making music and bringing it to the world is all about the power of originality amplified by teamwork. A great idea, like a great

The goal of the Dimensional Data Team (DDT) is to own and deliver dimensional data on concepts like Artists, Participants, Albums, and Tracks to business users and internal WMG products, empowering artist and music label decisions.

You have an undergraduate or graduate degree in Computer Science, Computer Engineering, or equivalent experience
You have at least 6 years of experience in backend or full stack development
You have a deep knowledge of AWS or other large cloud computing environment
You have strong distributed systems skills: How to make things fast and scalable, how to measure things (e.g. latency, throughput)

Testing, monitoring, paging, and incident management
You have a strong understanding of developing APIs, backend services, databases, data pipelines, data warehousing
And our renowned publishing company, Warner Chappell Music, represents genre-spanning songwriters and producers through a catalog of more than one million copyrights worldwide.

How to make things fast and scalable, how to measure things (e.g. latency, throughput)
You have strong operational excellence best practices: Testing, monitoring, paging, and incident management
Tech stack primarily consists of Python, Databricks, AWS, Postgres, Typescript, React, and Snowflake.
Experience in this tech stack is a plus although not mandatory
You have strong debugging skills and the ability to diagnose and resolve complex issues
In addition, WMG counts film and television storytelling powerhouse Warner Music Entertainment among its many brands.
Major Sound.
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Click the "Apply" link at the top of the page, or apply directly with your LinkedIn.
Thanks for your interest in working for WMG.
WMG is committed to inclusion and diversity in all aspects of our business.

At Warner Music Group, we're a global collective of music makers and music lovers, tech innovators and inspired entrepreneurs, game-changing creatives and passionate team members.
Here, we turn dreams into stardom and audiences into fans.
We are guided by three core values that underpin everything we do across all our diverse businesses:
Independent Minds.
Applying with LinkedIn will import all of the information you put in your profile, but will still allow you to upload a resume and cover letter.
Don't be discouraged if you don't hear from us right away.
We're taking our time to review all resumes, and to find the best people for WMG.
We love it here, and think you will, too.
We are proud to be an equal opportunity workplace and will evaluate qualified applicants without regard to race, religious creed, color, age, sex, sexual orientation, gender, gender identity, gender expression, national origin, ancestry, marital status, medical condition as defined by state law (genetic characteristics or cancer), physical or mental disability, military service or veteran status, pregnancy, childbirth and related medical conditions, genetic information or any other characteristic protected by applicable federal, state or local law.

This listing is sourced directly from Warner Music Inc.'s careers page and normalized into a canonical job model.