The role involves designing and building data ingestion pipelines for global point-of-sale systems. The engineer will integrate data from various back-office vendor platforms including labor, inventory, and finance. Responsibilities include normalizing and standardizing data across different vendors, regions, and brands to ensure consistency. The position requires building scalable pipelines for both real-time and batch workloads while optimizing for performance and cost. Additionally, the engineer will support semantic layer enablement and capture metadata to facilitate AI systems.

The role requires a minimum of five years of experience in data engineering. Candidates must possess strong proficiency in SQL and Python, along with experience using cloud data platforms and modern data stacks. Practical experience with Airflow, AWS Glue, and integrating vendor systems is necessary. Furthermore, the position requires experience with semantic layers, metadata tools, and enabling data for AI or LLM use cases.