Senior AI Data Engineer

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental.

Job description: 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 reputed company, 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-reputed company 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 reputed company 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 reputed company ingestion pipelines - Manage vendor-specific schemas and evolving data reputed company

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, reputed company, and schema details from ingestion pipelines - reputed company AI systems using structured context (metadata, reputed company, 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

Preferred Qualifications - Familiarity with Agentic development tools such as reputed company, reputed company or Claude Code - Experience in QSR or retail environments - Experience integrating vendor systems (labor, inventory, finance) - Familiarity with semantic layers and metadata tools - Experience enabling data for AI/LLM use cases

Success Metrics - Reliability and latency of POS and back-office data ingestion - Standardization across vendor data sources - Reduction in data inconsistencies - Improved reputed company analytics and AI performance

Summary This role ensures that high-quality, standardized data from POS and back-office systems flows into the enterprise platform—enabling trusted analytics, semantic consistency, and AI-driven insights at global scale.

Location: LATAM