Bolster is a digital, creative and product agency specialising in music, events and entertainment. We’re incredibly passionate about our clients and, hopefully, they’re on your radar too. We’re talking teams like Splendour in the Grass, St. Jerome’s Laneway Festival, Falls Festival, Chugg Entertainment, Fairfax Events, Pinot Palooza and Future Classic, plus global lifestyle brands such as YouTube, JBL, Sapporo and Blundstone.
We design and develop digital products for ourselves and our clients. Some notable mentions include Linktree, (an instagram tool with 800k users) and two webby award nominated projects: Buzz and LNWY - an online music publication we collaborated on with Laneway Festival.
We’re looking for a data analyst help lead a major change in the way the music industry consumes and understands data. You’ll work with a data Engineer to:
Analyse Bolster’s significant set of industry data from sources like Facebook, Adwords, Analytics and Spotify to find insights and present in publications.
Help the business grow through maintaining, modelling and presenting insights from our data warehouse.
Manage and help develop data tools like Buzz, an analytics feature for Linktree and leading reporting tools for the agency. Linktree has gained massive popularity in the last year, with 600k users and gaining over 3500+ new signups a day. Some key users are Alicia Keys, Eminem, The Grammys, Starbucks, Crossfit, Perez Hilton, Fender + Kahlua.
Our dream candidate will possess all of the following qualities :
Advanced SQL with working experience in dimensional modelling
Experience with a web analytics tool like Snowplow
Visual design experience, particularly with data visualisation
Familiar with one BI tool (Tableau, Looker, Mode, Metabase etc)
An interest in music and the industry
Personable, clear and constructive communication skills.
Bonus Points :
Experience with Python or R
An interest in Machine Learning and predictive analytics (experience even better!)
Knowledge of NoSQL, particularly ElasticSearch highly desired
Knowledge of the big data tooling landscape and best practices