Data Engineering- Intelligent Operations (T4/T3)

Intelligent Operations | CIS FDE Technology & Operations

"We support and guardrail AI forward deployed engineering engagements across SAP industries — ensuring quality, security and lasting impact."

In this role, you belong to the Intelligent Operations pillar of T&O — the operational backbone of CIS Forward Deployed Engineering. Your job is to build and maintain the data infrastructure that keeps deployed AI solutions reliable, governed, and continuously improving.

What you’ll bring

You’ve built and operated the data infrastructure that AI systems depend on — not experimental notebooks, but production data pipelines that feed agents, models, and customer-facing applications at scale. You have deep expertise in data pipeline development, data governance, and data infrastructure management across cloud-native environments.

You understand that data quality in AI-native operations is fundamentally different from traditional data engineering. The data flowing through agentic systems needs to be validated, governed, and traceable — not just available. Experience with knowledge graph integration, master data management and customer data handling in enterprise environments gives you a head start. What’s non-negotiable is that you know how to build data infrastructure that’s production-grade and scales without manual intervention.

You can work across organizational boundaries — with application engineers, platform teams, delivery squads, and data science teams — and you’re comfortable defining data standards and governance practices that others will follow. You’ve done this before: not just built pipelines, but built the data layer that production systems trust.

What you’ll build

You’re building the data backbone for Intelligent Operations. Every AI solution CIS deploys depends on data — for inference, for evaluation, for drift detection, for continuous improvement. That data layer is your domain.

Concretely:

• Production data pipelines: Design, build, and maintain the data pipelines that feed our own systems based on deployed AI agents and applications. This includes ingestion, transformation, enrichment, and delivery — all automated, monitored, and resilient. • Data governance and compliance: Establish and enforce data governance practices across customer engagements. Data privacy, data boundary enforcement, lineage tracking, and audit trails — baked into the infrastructure, not managed in spreadsheets. Regulated industries are a significant part of our customer base. • Knowledge graph and semantic data: Build and maintain knowledge graph infrastructure that powers our internal agent context and retrieval. As IAI solutions grow more agentic, the quality of structured knowledge becomes a differentiator in our own production processes — not just in demos. • Data quality and validation: Automated validation frameworks for production data. Drift in data quality is the silent killer of AI system performance. You’ll build the checks that catch degradation before it impacts our agentic solutions or customer SLAs. • Data product lifecycle: Treat data assets as products with versioning, documentation, ownership, and lifecycle management. Work with the Knowledge & IP team to ensure data assets are reusable across engagements — so the next customer project doesn’t start from zero.