Bench - EDW Data Engineer - INTL

• Strong written + verbal communication (will be asking these resources to work directly with project management, product management, stakeholders to gather requirements, work through testing/validations, etc. They need to be able to drive the project forward, work independently and be okay interacting with end-users and key stakeholders)
• Ability to speak to the outcomes they have driven within their domains through their queries
• SQL expert
• ETL/BI development experience
• GCP
• BigQuery
• Python
• Dataform and/or DBT
• Must be a systemic-thinker Experience with Airflow for Orchestration

Agentic AI tooling experience

A Fortune 100 retail client of Insight Global is looking to hire elite Data Engineers to join their Enterprise Data Warehouse Team. This team is responsible for supporting and providing all 12 company business units with the data they need to optimize their BUs and increase efficiency/profitability. This company has a centralized data organization and is Google's largest customer. There are over 200 people that report to this director, and his teams are responsible for working with over 170 petabytes of data within BigQuery. This Data Engineer will be responsible for legacy redesigns, net new projects within all 12 business units, and will be using cutting-edge technology for all of it. The ideal candidate for this role can vary in years of experience but must be able to think systemically about computer science instead of just being able to code. For example, there are a hundred ways to cut code, but these candidates must understand what the trade-offs are of doing it each way and be able to explain why they would decide which way they chose to cut it. We are looking for someone with a growth mindset who is able to reflect on their prior work and think of what they would do differently next time to improve it. The overarching goal of this team is not just build a data set, but to build one that is robust using processes and techniques that will allow to work just as well on day 100 as it would on day 1.