Sr. Analytics Engineer

ABOUT THE ROLE

The Enterprise Data team at Peloton works with many teams to get the most out of their data. As an Analytics Engineer you’ll work with stakeholders to build models the right way and serve as a thought leader on best practices around analysis. Peloton is looking for a talented individual to join the team to build and maintain foundational data infrastructure essential to gaining insights. This person will be instrumental in shaping the team’s technical direction and growth.

At Peloton, we’ve invested in a modern data tech stack (Redshift, Airbyte, Airflow, dbt, Looker) and a world-class data team. If you’re excited about utilizing the best technology out there and being part of a growing team that values collaboration, proactivity, disciplined decision making, and knowledge sharing, we’d love to hear from you.

YOUR DAILY IMPACT AT PELOTON

• Architect performant, accurate, and scalable dimensional data models using dbt and LookML using DRY (don’t repeat yourself) principles
• Set up tests to ensure data quality, monitor daily job execution, and diagnose/fix issues to ensure SLAs are met with internal stakeholders
• Troubleshoot, diagnose, and resolve issues with data pipelines urgently and thoroughly
• Understand business context and support analysts/business stakeholders in creating and standardizing source-of-truth Looker Explores and Dashboards
• Document your designs, prioritize data governance, and evangelize best practices

YOU BRING TO PELOTON

• 5+ years experience in an analytics engineering, data engineering, business intelligence, or technical data analytics role supporting a variety of business domains. Serious plus: You’ve worked with subscription and/or ecommerce based business models.
• Demonstrated experience helping teams streamline and scale with improvements to technical design, code review, and analytics architecture
• Expertise in SQL and ETL optimization techniques, especially within cloud-based data warehouses like Redshift, as well as use of OSX command line and version control software. Plus: Knowledge of AWS ecosystem/CLI and data ingestion tools (e.g. Fivetran, Stitch)
• Experience building Looker data models (LookML), data pipeline management technologies with dependency checking (e.g. Airflow, dbt), schema design, and dimensional data modeling
• Ability to leverage tools, business intuition, and attention to detail for data validation and QA
• Excellent communication skills, particularly when explaining technical or complex matters to less technical co-workers. Ability to document design and technical specifications in a clear and concise way.

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