Forward Deployed Data Engineering (Chief) Expert

SAP•Stanford, CA

1d•$274,300 - $609,200•Hybrid

About The Position

In this role, you define and drive enterprise scale data engineering strategies and architectures that enable business critical analytics, AI, and innovation embedded directly within customer environments and fast-moving delivery teams. You are accountable for the architectural integrity and delivery of high impact data engineering outcomes across complex initiatives, ensuring alignment between technical decisions and strategic business goals. You work closely with customers and combine cloud native software engineering, enterprise data engineering, and production grade AI capabilities to solve complex real world business problems at speed. Acting as a senior technical authority and strategist, you lead the design of scalable, secure, and resilient data architectures across SAP and non-SAP landscapes. Your work directly influences SAP’s competitive advantage by ensuring that data assets are reliable, governed, and optimized for enterprise wide use.

Requirements

Demonstrate a proactive approach to leveraging AI in everyday workflows, ensuring high-quality outputs through thoughtful context design and system integration.
Deepest level of professional expertise in data engineering, with broad experience across architectures, platforms, and large-scale data ecosystems.
Proven ability to act as a technical strategist, defining reference architectures, standards, and long-term data engineering roadmaps.
Proven ability to work in an Agentic AI context, shaping and applying AI agents that act autonomously within defined boundaries, collaborate with human stakeholders, and deliver measurable business outcomes in customer-centric environments.
Strong track record of delivering complex, innovative, and business-critical programs spanning multiple teams or initiatives.
Advanced capability in data architecture, data governance, security, and privacy within enterprise environments.
Ability to influence and advise senior stakeholders and executives on high-impact technical and strategic decisions.
Recognition as a thought leader or "go-to" expert in data engineering, contributing to knowledge sharing and best practices internally and externally.
Excellent communication and collaboration skills, enabling alignment across diverse, cross-functional teams.

Responsibilities

Define and govern enterprise scale data engineering architectures, standards, and reference designs across platforms and initiatives.
Lead the design and evolution of scalable, secure, and resilient data platforms, pipelines, and frameworks.
Embed directly into customer projects and fast moving cross functional teams to rapidly understand business processes, technical landscapes, and operational challenges.
Act as the escalation point for complex technical challenges, resolving high impact issues and guiding critical architectural decisions.
Drive alignment between business strategy and data engineering roadmaps, anticipating future needs and emerging technologies.
Partner with senior stakeholders, architects, and leadership teams to translate complex technical concepts into clear strategic guidance.
Ensure data engineering solutions meet enterprise requirements for performance, security, governance, and compliance.
Influence long term technology direction by introducing innovative approaches and best practices in data engineering.
Act as a trusted technical advisor to customers and internal stakeholders, clearly communicating architecture decisions, implementation tradeoffs, technical risks, and delivery progress.
Architect and optimize scalable end-to-end data pipelines and ETL/ELT frameworks.
Lead enterprise-wide data modelling, orchestration, and cross-platform integration initiatives.
Leverage advanced Python and SQL expertise for high-performance data engineering solutions.
Design and implement distributed data processing solutions using PySpark and Databricks.
Drive workflow automation and orchestration using Airflow and enterprise scheduling platforms.
Deliver enterprise data solutions leveraging SAP HANA and modern data platform architectures.
Implement lakehouse architectures using Parquet, Delta Lake, and related storage technologies.
Build and optimize real-time streaming pipelines using Kafka and event-driven architectures.
Ensure enterprise-grade data quality, governance, compliance, and PII protection standards.
Apply DevOps best practices including CI/CD, Docker, and Git for scalable data engineering delivery.