AI Data Engineer

Taulia, a fintech company within the SAP group, helps businesses and their suppliers access cash and improve working capital through its global network. The AI Data Engineer will bridge the AI Center of Excellence and existing data teams by building custom integrations, reusable data pipelines, and retrieval architectures that provide secure, high-quality data to AI agents. The role also focuses on RAG optimization, data library management, data quality, and governance.

Responsibilities:

• Building and maintaining the custom connectors (APIs, ETL pipelines) required to extract data from core business systems for use in AI tools
• Working with system owners to unlock "siloed" data that is currently inaccessible to our AI ecosystem
• Designing and maintaining the data pipelines that feed our AI knowledge base
• Optimizing "Retrieval-Augmented Generation" (RAG) performance by improving how documents are chunked, tagged, and indexed to reduce hallucinations
• Ensuring data freshness so agents never act on obsolete information
• Creating and maintaining the "AI Data Library" - a comprehensive technical map of where our enterprise data lives, its schema, and its owner
• Working with business teams to identify "Dark Data" (valuable data trapped in PDFs or desktops) and bringing it into the AI ecosystem
• Implementing automated checks to monitor data quality and completeness
• Ensuring sensitive data (PII) is properly flagged and excluded from general AI access where appropriate

Requirements:

• Demonstrated experience integrating data with Enterprise Search engines and AI agents, or RAG-based systems
• Practical experience preparing data specifically for consumption by Large Language Models and agentic orchestration tools
• 5+ years in data engineering, ETL development, or database management
• Strong experience building custom API connectors and data ingestion scripts
• Experience working with unstructured data (text, documents) and NLP concepts
• Strong proficiency in SQL and Python
• Meticulous attention to detail—you care deeply about data cleanliness
• Understanding of enterprise knowledge management challenges
• Ability to audit data sources and identify gaps
• Strong communication skills to work with business owners on data access and cleanup