Senior AI Data Engineer

About the role

We are seeking an AI Data Engineer to design, build, and scale the data foundations that power AI, advanced analytics, and natural language interfaces across a global Quick Service Restaurant (QSR) enterprise. This is a high-impact role at the intersection of data engineering and applied AI, where the pipelines you build become the building blocks for use cases like automated insight generation, conversational analytics, and intelligent decision support at restaurant scale. A core focus of this role is data ingestion from POS systems and back-office vendor platforms spanning labor, inventory, finance, and supply chain. You will ensure reliable, standardized, and scalable ingestion of high-volume, multi-source data into the enterprise data platform, transforming fragmented operational data into AI-ready assets. You will also play a critical role in connecting raw data to business meaning, supporting governed KPI definitions and AI-driven context engineering through a robust semantic layer that enables both human analysts and AI agents to query, reason over, and act on trusted data. Key Responsibilities - Data Ingestion & Integration (POS & Back Office) - Design and build ingestion pipelines for POS systems across global markets (transactions, orders, payments, speed of service) - Integrate data from back-office vendor systems (labor scheduling, inventory, supply chain, finance) - Handle diverse ingestion patterns (batch, streaming, APIs, file-based ingestion) - Normalize and standardize data across vendors, regions, and brands - Ensure data freshness, completeness, and consistency across all ingestion pipelines - Manage vendor-specific schemas and evolving data contracts Key Responsibilities - Data Platform & Pipeline Engineering - Build scalable ELT/ETL pipelines using modern tools (DBT, Airflow, etc.) - Support real-time and batch processing for operational and analytical use cases - Optimize ingestion pipelines for performance, reliability, and cost - Ensure high availability across global data workloads Key Responsibilities - Semantic Layer Enablement - Build data models supporting standardized KPIs (same-store sales, speed of service, traffic, labor productivity) - Align ingested raw data with business-friendly semantic abstractions - Support both governed (manual) and AI-driven semantic approaches Key Responsibilities - Metadata & Context Engineering - Capture metadata, lineage, and schema details from ingestion pipelines - Enable AI systems using structured context (metadata, lineage, example queries) - Ensure AI systems can reliably interpret POS and operational data Key Responsibilities - Governance & Data Quality - Implement validation and monitoring for ingestion pipelines - Ensure data accuracy across POS and vendor systems - Support governance for consistent metric definitions across regions - Maintain versioning and schema evolution processes Required Qualifications - 5+ years of experience in data engineering - Strong SQL and Python skills - Experience building ingestion pipelines from APIs, streaming, and batch sources - Experience with POS or operational data systems preferred - Experience with cloud data platforms and modern data stack