Senior Analytics Engineer, Metropolis

• 5+ years of experience in data/analytics engineering with proficiency in Python and SQL within Snowflake
• 3+ years of experience with dbt
• Owned large-scale data projects that drove significant impact through effective cross-functional partnering
• Led the design, implementation, and monitoring of data warehouses that enable clear product-focused insights
• Expert at SQL, window functions, and query optimization for complex data manipulations
• Applied performance best practices, including partitions, sort keys, and incremental modeling
• Designed systems using test-driven approaches to ensure data quality and flow integrity
• Familiarity with ELT processes and tools to prepare and clean data for analysis
• Skill in using tools like dbt to build data transformation pipelines
• Experience mentoring stakeholders in Data Science, Analytics, PM, and Engineering on data best practices

Desirable

• Metropolis is seeking a Senior Analytics Engineer to join our Data Engineering and Analytics team. The ideal candidate will possess a passion for creating value using data and a strong foundation in all aspects of analytics engineering
• In this pivotal role, you will be instrumental in making data accessible and actionable, facilitating informed decision-making throughout the company. You will partner and lead across multiple product team functions and domains and drive impact through your data vision and architecture
• Design and implement large-scale data warehouses and data marts using Snowflake, Star, or Data Vault 2.0 schemas
• Build and maintain data transformation pipelines using SQL frameworks like dbt to abstract model code
• Write complex SQL including window functions and STRUCT/ARRAY manipulations while adhering to performance best practices
• Manage and optimize DAGs, including troubleshooting task failures and improving run times
• Implement metadata management, data lineage, and security principles such as least-privilege access
• Use a test-driven approach to design systems that trap poor quality data and trigger alerts within the data flow
• Partner with engineering, data science, and business stakeholders to own project scopes
• Mentor others on data best practices to drive impact through your data vision
• Provide architectural guidance to ensure data is accessible and actionable across the company
• Drive technical excellence within the data engineering and analytics domain