AI Data Engineer

About SAP Taulia

Taulia is a fintech company founded in 2009 that helps businesses manage payments and cash flow more efficiently, and it became part of SAP in 2022. It mainly focuses on solutions like early payments, invoice financing, and supply chain financing, allowing suppliers to get paid faster instead of waiting for long payment cycles such as 30–90 days. In simple terms, Taulia acts as a bridge between companies and their suppliers, improving cash flow for both sides and strengthening business relationships, and since it is integrated with SAP’s enterprise systems, it plays an important role in large organizations handling finance and supply chain operations.

Job Description

About the Job

AI is only as smart as the data it consumes. We are seeking an AI Data Engineer to ensure our agent ecosystem is powered by high-quality, structured, and retrieval-ready data. You will serve as the primary bridge between our AI Center of Excellence and existing data teams, coordinating data readiness and architecting the custom integrations or interfaces needed to expose business data to AI tools. By building and managing reusable data pipelines and retrieval architectures, you ensure our AI agents can access the right information securely and performantly, working in lockstep with the AI Solutions Architect to bring high-value agents to life.

Responsibilities

Custom Data Integrations:

• 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.

Data Pipeline & RAG Optimization:

• 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.

Data Library Management:

• 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.

Data Quality & Governance:

• Implementing automated checks to monitor data quality and completeness.
• Ensuring sensitive data (PII) is properly flagged and excluded from general AI access where appropriate.

Skills, Knowledge and Experience required

• AI & Search Ecosystem Experience: Demonstrated experience integrating data with Enterprise Search engines and AI agents, or RAG-based systems. You understand what makes data "searchable" for an AI.
• LLM & Agent Familiarity: Practical experience preparing data specifically for consumption by Large Language Models and agentic orchestration tools. You know how to structure data to minimize hallucinations.
• Data Engineering Experience: 5+ years in data engineering, ETL development, or database management.
• Integration Expertise: Strong experience building custom API connectors and data ingestion scripts.
• Unstructured Data Expertise: Experience working with unstructured data (text, documents) and NLP concepts.
• SQL & Scripting: Strong proficiency in SQL and Python.

Skills

• 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.