About Loop Loop is industrializing services through AI, starting with one of the world's most complex industries: the global supply chain. We're an AI-native company built to solve the systemic data problem that's held logistics back for decades. Backed by Index and a16z.
About the role
As an Analytics Engineer at Loop, you will play a pivotal role in maturing the data organization. You’ll work cross-functionally across Engineering, Product, Design, Strategy & Operations, and AI Platform teams to design, build, and own the core infrastructure and data models. These systems will enable both internal and client facing analytics, accelerating data-driven decision-making throughout the company and our clients.
What they're looking for
- Own Core Data Models and ETL Pipeline s: Design, build, and maintain Loop’s core data models and ETL processes
- Define Metrics and Governance : Establish and govern key business metrics for consistency across teams
- Maintain Data Quality and Uptime : Ensure data accuracy and reliability with strong SLAs
- Automate Data Requests : Convert ad-hoc data requests into scalable, repeatable pipelines
- Optimize Data Infrastructure : Manage and optimize data infrastructure for performance, cost, and compliance
- Develop Data Products : Create data-driven solutions and embedded analytics to support business and product teams
More about this role
About the Role
As an Analytics Engineer at Loop, you will play a pivotal role in maturing the data organization. You’ll work cross-functionally across Engineering, Product, Design, Strategy & Operations, and AI Platform teams to design, build, and own the core infrastructure and data models. These systems will enable both internal and client facing analytics, accelerating data-driven decision-making throughout the company and our clients.
This is an in-person role based in our Chicago office with the expectation of being onsite 4+ days per week.
What You’ll Do
- Own Core Data Models and ETL Pipeline s: Design, build, and maintain Loop’s core data models and ETL processes.
- Define Metrics and Governance : Establish and govern key business metrics for consistency across teams.
- Maintain Data Quality and Uptime : Ensure data accuracy and reliability with strong SLAs.
- Automate Data Requests : Convert ad-hoc data requests into scalable, repeatable pipelines.
- Optimize Data Infrastructure : Manage and optimize data infrastructure for performance, cost, and compliance.
- Develop Data Products : Create data-driven solutions and embedded analytics to support business and product...
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