Analytics Engineer

Requirements

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

• As an Analytics Engineer at Loop, you will play a pivotal role in maturing the data organization. You’ll work cross‑functionally 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 re‑usable 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.