Salary Range: 90000 to 125000 (Currency: USD) (Pay period: per-year-salary)
Dolls Kill is a global fashion brand that empowers everyone to celebrate their individuality. We currently have over 10 million social media followers including some of the most influential personalities in music, art, pop culture and fashion. We’re also backed by strong consumer-focused investors who share in our vision of building one of the world’s next great consumer brands.
Our team is growing quickly and we are hiring across many functions. We are looking for team members who are equally passionate about what we do and excited about joining the Dolls Kill crew.
About the Role:
In this role you’ll be hands-on managing big projects, with a smart passionate team, and deal with loads of data. You’ll lead integrating data from a variety of sources, transforming it for use, generating insights, and partnering with teams across the company to assure they have the information they need to make the best decisions.
We’re looking for a Financial Data Analyst who would handle advanced and complex excel modeling, sales data extraction to perform product productivity analysis, pricing and promotion analysis and other relevant customer based data to translate into meaningful action plans for continued growth.
Requirements:
·Strong analytical skills with the ability to collect, organize and analyze large amounts of data
·Experience with Looker or similar tool a must (Tableau, Power BI, etc..)
·Experience with ETL tools and processes
·Skilled at processing, cleansing, and verifying the integrity of data used for analysis; technical familiarity with data models and database design development
·Comfortable editing production data, close attention to detail
·Familiarity with common statistical models, experience with R or python a plus
·Experience with the following a plus: Matillion, PHP, Javascript, AWS
·Detail oriented, self-starter
·Experience leading and mentoring
·Adept at query writing, strong knowledge of and experience with MySQL a must
·Degree in Mathematics, Economics, Computer Science, Information Management or Statistics,
