Senior Analytics Engineer

Summary

Join RYZ Labs as a Sr. Analytics Engineer and contribute to a personal health brand revolutionizing daily essentials. You will build and maintain data infrastructure, focusing on dbt modeling, Snowflake optimization, and self-service analytics. This role requires bridging technical data infrastructure with business analytics needs. The ideal candidate possesses extensive experience in analytics engineering, dbt, Snowflake, and SQL. You will collaborate with business teams, ensuring technical excellence in data engineering and analytics engineering practices. This position offers the opportunity to work remotely and contribute to a dynamic, innovative startup environment.

Requirements

• 7-10 years of analytics engineering experience with focus on business intelligence and data modeling
• Advanced proficiency with dbt (data build tool) including macros, packages, testing, and deployment
• Deep experience with Snowflake including performance optimization, cost management, and advanced features
• Advanced SQL capabilities with complex query optimization and performance tuning
• Strong proficiency with Looker, Tableau, or similar BI platforms (Omni experience a plus)
• Experience with data quality frameworks, testing, monitoring, and documentation standards
• Proficiency with Git workflows, CI/CD processes, and analytics engineering best practices
• Experience building enterprise-grade BI solutions and data warehouses

Responsibilities

• Design, build, and maintain dbt models for business intelligence and analytics use cases
• Optimize Snowflake warehouse performance, cost management, and data architecture
• Build and maintain Omni dashboards, reports, and self-service capabilities for business teams
• Implement data quality checks, monitoring, and governance processes across all data assets
• Work closely with analysts and business stakeholders to understand requirements and deliver technical solutions
• Establish best practices for analytics engineering, code reviews, and documentation standards
• Partner with engineering teams on data pipeline improvements and integration of new data sources
• Monitor and optimize query performance, data model efficiency, and warehouse costs

Preferred Qualifications

• Experience with AWS, GCP, or Azure data services and cloud-native data tools
• Python or R for data analysis, automation, and advanced analytics
• Experience with Airflow, Fivetran, Stitch, or similar data orchestration tools
• Understanding of marketing, product, and customer analytics use cases
• Ability to explain technical concepts to non-technical stakeholders
• Experience working effectively with distributed teams across time zones
• Experience with data observability and monitoring platforms (Monte Carlo, Great Expectations, etc.)
• Advanced knowledge of warehouse optimization and cost management strategies