Join our growing Data Analytics team as a Data Analyst. In this role, you will develop and manage crucial data models, create reports and data visualizations, and collaborate with cross-functional teams to drive continuous improvement. You will have opportunities for growth and leadership as we expand. Benefits include a paid sabbatical, exclusive employee discounts, 401k match, work from home opportunities, generous paid time off, universal paid parental leave, competitive stakeholder pension, comprehensive health and dental care, company-wide events, and hybrid work options.
About the role
- 3+ years of hands-on experience with large, complex data sets - Experience with DBT for transforming and managing data models - Experience with dimension modeling and data architecture, ensuring scalable and efficient data structures - Passion for data and its fundamental ability to create value - Strong analytical skills, with the ability to uncover insights and communicate findings effectively - Ability to communicate effectively with a wide range of audiences, including business stakeholders, data engineers, and product teams - Bachelor’s degree in a quantitative field (e.g., Computer Science, Physics, Mathematics, Statistics…); advanced degrees and/or specializations a plus - Extremely curious and excited to dive into complex problems - Experience with code management and deployment on Github a plus - Experience with data analytics tools such as Looker or Tableau to build dashboards and reports - Experience working with event data or user/product analytics tools (e.g., Heap) is a plus - Highly proficient in SQL, with the ability to write efficient, optimized queries for large-scale datasetsKey missions
- Develop and manage crucial data models that describe customers and business, partnering cross-functionally with business leaders.
- Own the creation of rich reporting and data visualizations, enabling functional leaders to quickly digest information and make data-driven decisions.
- Collaborate closely with Data Engineering to design, optimize, and maintain data pipelines, ensuring data availability, accuracy, and efficiency.