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# Analytics Engineer

- **Company:** Loop LLC
- **Location:** Chicago, IL, United States
- **Experience:** Experienced
- **Salary:** $140,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Analysis, Data Infrastructure, Extract Transform Load (ETL), Data Visualization, Uptime, SQL Databases, Tableau (Software), Delivery Pipeline, Snowflake, AI Platforms, Core Data, Data Analytics, Data Management, Looker Analytics
- **Published:** August 10, 2026
- **Apply:** https://www.adzuna.com/details/5834303586

## About the Role

* 2+ years experience building analytical products (system, dashboard, models).
 * Strong proficiency in SQL and Python for data transformations and automation.
 * Hands-on experience with modern data stack tools (e.g., Snowflake, dbt, Dagster, Airflow, Looker).
 * Excellent business communication skills to translate data insights into actionable business strategies.
 * Experience with data dashboarding and visualization tools (e.g., Tableau, Looker, Superset, Mode).
 * Ability to manage, optimize, and scale data infrastructure.

Nice to Have

 * Global supply chain experience in transportation and logistics.
 * Previous experience setting up a data stack in a startup from scratch.
 * Interest or experience in working with cutting-edge open-source data tool.
 * Proven ability to convert ad-hoc data requests into scalable, automated pipelines.
 * Experience working in the logistics domain.

## Description

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., * Own Core Data Models and ETL Pipelines: 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.


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