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.

You’ll work closely with the Application Engineering and Platform Engineering teams. They operate the solutions and the infrastructure — you ensure the data underneath is solid, governed, and continuously improving.

Where you belong

T&O’s mission: ensure quality, security, and lasting impact across AI engagements for SAP’s industry customers. The CTO Office defines how we build. T&O manages what CIS has built — ensuring every engagement is production-grade and reusable.

Within Intelligent Operations, you own the data layer that everything else depends on. Without reliable, governed data infrastructure, there’s no trustworthy AI in production — just models running on hope.

This is a technical leadership role. You’ll define data engineering standards, mentor engineers, and shape the data strategy for a capability that’s being stood up now. You’ll work globally, across FDE squads and hubs, with external delivery partners, and with the SAP data and analytics ecosystem when integration questions need answers.

If you’ve spent your career making data infrastructure production-grade — and you want to define what data engineering looks like for enterprise AI operations — this is the role.

We value the skills you bring and support you to build on them through our learning and development opportunities. These are the skills associated with this role:

Agentic AI Day-to-Day Practice, AI Adoption Capability, AI Output Quality Assurance, Context Engineering, AI-Assisted Automation and Prototyping, Learning Agility, Collaboration, Complex Problem Solving, Creative Thinking, Effective Communication, Data Governance, Data Pipeline Development, Knowledge Graph Integration and Interaction, Data Product Lifecycle Management, PII Handling

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