Analytics Engineer Intern


We’re Looking For An: Analytics Engineer Intern to support the data-driven decision-making process of the organization by providing high-quality, reliable, and actionable insights through the design and implementation of efficient and effective data pipelines. The internship will provide the opportunity to gain insight on the daily operations of this career by working directly with the Manager of Analytics and team Data Scientist.

Location: Woodland Hills, CA (Hybrid or Remote)

What You’ll Be Doing:

  • Provide support to the analytics team in designing, building, and maintaining data pipelines

  • Learn and work with data warehousing and business intelligence tools such as BigQuery, DBT, and Tableau

  • Work on developing analytics dashboards, reports, and automated data feeds to support business decision-making

  • Assist with various data requests from inside and outside the department 

  • Attend team meetings to understand how data is used at Golden Hippo and learn how the team operates daily

Preferred Qualifications (Note: These are preferred and not required. We strongly encourage you to apply even if you don’t tick ALL of these boxes.):

  • Strong analytical and critical thinking skills

  • Ability to code in Python

  • Ability to write SQL queries 

  • Familiarity with visualization tools like Tableau or Salesforce 

  • Knowledge of Google Suite: Google Sheets/Google Docs/Google Drive

  • Ability to write SQL queries 

  • Ability to code in Python

  • Bachelor’s Degree or in last 2 years of degree

If you'd like to learn more about internships at Golden Hippo, click here to read about our current Employer Branding Intern’s experience!

*Following the conclusion of your internship, Interns will be invited to join our exclusive LinkedIn Alumni group where you will be updated on Golden Hippo announcements, job opportunities, and other industry-related information. 

The anticipated salary range for this position is $19-$22/hour. Salary is based on a wide range of factors that include relevant experience, knowledge, skills, other job-related qualifications, and geographical location.


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