Remote

Who We Are: 

We are a team of insights specialists that focus on finding insights relevant to driving business outcomes across the Calm organization. For us, the business problem comes first (what are we trying to solve?) and the analysis follows (how do we best solve this problem?). Sometimes the solution is straightforward and sometimes it is highly complex, but in all cases we rely on data to drive our solutions. 

What We Do:

As an Analytics Engineer you will take a leading role in the designing and developing scalable data models that power analysis, experimentation and reporting across our entire business. You will collaborate with key stakeholders across our product, engineering, operations and data science teams to understand our various first and third party data sources and translate this knowledge into performant analytical data models to be consumed by these respective partners.

What You’ll Do:

• Design and build data models used for experimentation, machine-learning, analysis and reporting our key metrics
• Collaborate with partners across technical and non-technical teams to bridge the gap between data and action 
• Help key-decision makers stay informed about the state of the business through internal data products
• Partner with our data science team to drive strategic decisions through data-driven analysis
• Own the development, testing, documentation and evangelism for our core data models
• Utilize analytical tools such as Mode and Tableau to help your business and data science partners to build actionable insights
• Develop strong cross-functional partnerships across Calm to drive our team's success

Who You Are: 

• 4+ years of analytics / data engineering experience
• Strong proficiency in SQL
• Prior experience in Python
• BA/BS in quantitative field
• Experience with dbt, and Apache Airflow is is a plus
• Ability to translate non-technical business requirements into technical solutions, and translate technical solutions to business outcomes
• Strong relationship management and presentation skills
• Hands on experiencing building data documentation and testing practices
• Pragmatic: balance scrappiness and rigor