Senior Analytics Engineer

About the role

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy - a future where mundane repetition disappears and being known unlocks access, comfort and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn't coming; it's here, and we need builders, innovators and problem solvers to help us create it.

Responsibilities

• 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

Requirements

• 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
• While not required, these are a plus:
• A dbt certification
• Metropolis may utilize an automated employment decision tool (AEDT) to assess or evaluate your candidacy for employment or promotion. AEDTs are used to assist in assessing a candidate's application relative to the required job qualifications and responsibilities listed in the job posting.
• As part of this process, Metropolis retains data relevant to your candidacy, including personal information, for a period that is

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