8 days ago
Garching bei München, GermanySenior
Responsibilities
- Design, build, and maintain automated, monitored, resilient production data pipelines for deployed AI agents and applications.
- Establish and enforce data governance, privacy, boundary controls, lineage tracking, and audit trails across customer engagements.
- Build and maintain knowledge graph infrastructure supporting agent context and retrieval.
- Develop automated data validation and quality frameworks to detect degradation before it affects AI solutions or customer SLAs.
- Manage data assets as products through versioning, documentation, ownership, reuse, and lifecycle controls.
- Define data engineering standards, mentor engineers, and shape data strategy for the Intelligent Operations capability.
- Collaborate with application engineering, platform engineering, delivery squads, data science teams, Knowledge & IP, SAP data and analytics teams, and external delivery partners.
Requirements
- Deep experience building and operating production data infrastructure and pipelines at scale.
- Expertise in data pipeline development, data governance, and data infrastructure management across cloud-native environments.
- Experience with ingestion, transformation, enrichment, delivery, monitoring, automation, and resilient pipeline operation.
- Understanding of AI-native data requirements for inference, evaluation, drift detection, and continuous improvement.
- Experience with knowledge graph integration, master data management, and enterprise customer data handling.
- Ability to establish data standards and governance practices across organizational boundaries.
- Experience handling data privacy, PII, data boundaries, lineage, audit trails, and regulated-industry requirements.
- Experience defining data engineering standards, mentoring engineers, and providing technical leadership.
- Skills associated with the role include agentic AI practice, context engineering, AI output quality assurance, data product lifecycle management, knowledge graph interaction, data governance, data pipeline development, PII handling, collaboration, communication, and complex problem solving.
Benefits
- Learning and development opportunities to build on existing skills.
- Flexible working models, health and well-being support, and an inclusive workplace.
- Global collaboration across FDE squads, hubs, delivery partners, and SAP’s data and analytics ecosystem.
Categories
Data Engineering
About SAP
SAP builds enterprise application software and cloud services for finance, procurement, supply chain, human resources, and analytics, sold through subscriptions, licenses, and support. Its portfolio includes SAP S/4HANA, SAP Business Technology Platform, SAP Ariba, and SAP SuccessFactors. Founded in 1972 and headquartered in Walldorf, Germany, SAP is a public company listed on the NYSE and Frankfurt (ETR) and serves enterprises and public-sector organizations worldwide.