Senior Analytics Engineer, Calm

• Extensive experience with data modeling and analyzing large scale data with modern cloud computing platforms. Experience with dbt strongly preferred
• Strong proficiency in SQL
• Experience with data pipeline development tools in a modern data stack such as dbt, Databricks, BigQuery, and Airflow
• Prior experience in Python
• 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
• Pragmatism: balancing scrappiness and rigor
• 8 years of relevant experience
• 4+ in data science, data engineering or analytics engineering

Desirable

• As a Senior Analytics Engineer it is your directive to design and develop scalable data models that power our data products serving internal 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
• Own and improve our dbt implementation and data models, turning raw data into scalable, business-ready datasets that enable clear analysis of business and platform performance
• Collaborate with partners across technical and non-technical teams to bridge the gap between data and action
• Inform key decision makers about the state of the business through internal data products
• Own the development, testing, documentation and evangelism of our core data models
• Utilize analytical tools such as Databricks, BigQuery, Airflow, Mode and Tableau to help our business and data science partners to build actionable insights
• Develop strong cross-functional partnerships across Calm to drive success
• Some past projects include:
• Partnering with Data Engineering to set up a reporting system in BigQuery from scratch. This included data replication, infrastructure setup, dbt model creation, and integration with reporting endpoints
• Developing and implementing an alerting system for critical metrics
• Architecting the data warehouse to serve many internal data consumers (e.g. analysts, engineers, product managers, operations managers)
• Building an efficient and scalable data pipeline for high-volume events in the data warehouse