Senior Analytics Engineer, Rula

• 4+ years of professional experience in analytics engineering, data engineering, or highly technical data analytics roles
• Expert-level SQL proficiency and deep data modeling experience, with a proven ability to write performant queries and understand the architectural trade-offs of database design decisions
• 2+ years of dedicated, hands-on experience building, deploying, and maintaining modular data pipelines using dbt in a production environment
• Proven experience designing schemas and optimizing query performance on modern cloud data platforms (such as Snowflake, Redshift, or BigQuery)
• Absolute comfort with production git workflows (branching, merging, pull request reviews) and automated data quality testing practices
• Experience working in a cross-functional role partnering directly with analytics, operations, or business teams, where strong requirement-gathering and the ability to translate complex data structures into clear documentation are crucial
• While having the preferred qualifications below enhances your candidacy, having all of them is not mandatory. We encourage all interested applicants to apply, even those who may not meet every preferred requirement
• Hands-on experience or deep familiarity with Snowflake architectures and running data migrations to Snowflake environments

Desirable

• We are hiring a Senior Analytics Engineer to join our unified, high-velocity Analytics Engineering pod
• In this role you will partner with a talented team of engineers to co-own, scale, and optimize the "Transform" layer of our data ecosystem across modern data platforms
• This is a core technical engineering role. You will balance deep code execution in SQL and dbt with strong cross-functional stewardship, translating complex business processes into trusted, production-grade data models
• The ideal candidate will bring a strategic mindset, excellent code hygiene, and the professional backbone required to manage competing technical priorities while navigating highly regulated data systems
• Data Modeling & Architecture: Design, build, and optimize modular dbt pipelines and performant technical schemas, setting the team standard for version control, unit testing, and velocity
• Cross-Functional Translation: Act as a critical technical bridge between Analytics, Operations, and Data Engineering to transform ambiguous product requirements into robust data assets
• Platform Optimization: Diagnose performance bottlenecks to slash query latencies and lower warehouse compute costs across our modern data platforms, specifically aiding our strategic migration toward Snowflake
• Team Stewardship: Elevate the entire pod’s engineering culture by participating in rigorous peer code reviews, refactoring legacy technical debt, and enforcing strict data security standards up to the point of delivery
• Culture Multiplier: Actively mentor and pair with junior peers to unblock complex bugs, optimize engineering workflows, and natively adopt AI-assisted development tools to accelerate test generation and legacy code discovery