Analytics Engineer at Yousician
Helsinki, , Finland -
Full Time


Start Date

Immediate

Expiry Date

10 Dec, 25

Salary

0.0

Posted On

12 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

JOIN YOUSICIAN

Yousician impacts and enriches the lives of millions of people by empowering them to play music they love, learn something new, and nourish their well-being. We merge music and tech to amplify the everyday. We reimagine the way people learn to play so that everyone can play.
And, you don’t need to be a musician to join Yousician!
We’re a band of passionate professionals who love what they do, are great at it, and move to the beat of the same drum. We’re for all players; from newbies to seasoned pros. If you are looking for a rewarding career opportunity that helps shape the future of music-tech, you’re in the right place!

ABOUT US

Yousician is the world’s largest music-tech company, with millions of users worldwide. Based in Helsinki, Finland, Yousician is home to the category-defining Yousician and GuitarTuna apps.
Yousician, the #1 platform to learn and play music, makes learning fun, easy, and more accessible for everyone. Yousician’s innovative, award-winning technology listens to you play and gives instant feedback. With interactive, gamified lessons and songs, and courses by world-famous artists like Metallica, Billie Eilish and more, Yousician empowers people to play music they love.
Today Yousician employs 120+ professionals from nearly 30 nationalities.
We’re hiring, so come join our amazing team and help us make the world a more musical place!

Responsibilities
  • Design and development of data models in various tools (DBT and LookML)
  • Development of the “last mile” for analytical data, supporting the connection between the Data Engineering team tools and internal stakeholders
  • Helping analysts to utilise and get value from data modeling and semantic layers and providing help in the identification of common problems across the teams and developing general solutions and standardised approaches
  • Make optimal use of the data infrastructure and contents, both architectural and performance wise
  • Improving the process of development and quality monitoring of data modelling layer
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