Scope of the Role: We are seeking a Data Engineer to design and build enterprise data warehouses, data lakes, and pipelines that power data-driven decision-making for data center supply chain and real estate operations. This role is responsible for creating scalable, secure, and optimized ETL infrastructure on GCP/AWS, while enabling advanced AI/ML use cases such as RAG, copilots, and agentic AI for predictive analytics and workflow automation.
What You'll Own:- Design and implement data-driven solutions on GCP including BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Looker/BI.
- Build ETL scripts using SQL and Python to extract, clean, and transform structured and unstructured data from ERP, procurement, logistics, and facility management systems.
- Develop and optimize data pipelines for ingestion, transformation, and loading into enterprise data lakes and warehouses.
- Build and extend end-to-end data and BI solutions, spanning extraction, storage, transformation, and visualization layers.
- Partner with supply chain, real estate, and AI/ML teams to provide pipelines for AI solutions (e.g., RAG ingestion, Copilot integration, multi-agent workflows).
- Ensure data governance, lineage, and compliance across supply chain datasets.
- Continuously optimize query performance, ETL processes, and pipeline reliability.
You'll Thrive in This Role If You Have:- Advanced proficiency in SQL (complex queries, optimization) and Python (data engineering, scripting, APIs).
- Experience building ETL/ELT pipelines operating on structured and unstructured data sources.
- Knowledge of enterprise data warehouse and data lake architectures.
- Exposure to data pipelines for AI/ML (vector DB ingestion, embeddings, RAG pipelines, copilots, agents).
- Familiarity with supply chain or data center operations data is a strong plus.
- Bonus: experience with ML Engineering, data visualization tools (Looker, Tableau, Power BI) and MLOps practices.
- Strong hands-on expertise with GCP services: BigQuery, Dataflow, Pub/Sub, Cloud Storage, Looker/BI (or similar, preferred).
