Forward Deployed Data Engineer Expert (f/m/d)

We help the world run better
At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. 

 

What you’ll build

In this role, you design, build, and operate scalable data pipelines and platforms that enable analytics, reporting, and advanced data driven use cases embedded directly within customer environments and fast-moving delivery teams. You translate business and analytical requirements into robust, maintainable data solutions and ensure that data is reliable, secure, and accessible across the organization. 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.

You are accountable for the technical quality, scalability, and business impact of delivered solutions within customer engagements. You operate at the intersection of engineering, data, AI, and customer delivery, collaborating closely with product, architecture, ML, and customer teams to accelerate adoption of AI driven enterprise solutions while maintaining alignment with SAP engineering and security standards.

 

In this role, you will:

• Design, develop, and maintain scalable data pipelines and data models that support analytics, reporting, and AI use cases.
• Integrate data from multiple internal and external sources, ensuring data quality, consistency, and reliability across the data landscape.
• Build and optimize data processing workflows using modern data platforms and cloud based technologies.
• Ensure data solutions meet enterprise requirements for performance, security, governance, and compliance.
• Embed directly into customer projects and fast moving cross functional teams to rapidly understand business processes, technical landscapes, and operational challenges.
• Collaborate with data scientists, analysts, and product teams to translate analytical requirements into efficient data engineering designs.
• Monitor, troubleshoot, and improve data pipelines and platforms to ensure operational stability and continuous improvement.
• Capture reusable patterns, accelerators, and product improvement opportunities from customer engagements, contributing back to engineering standards, frameworks, and best practices across teams.
• Contribute to engineering standards, best practices, and reusable components to increase efficiency and maintainability

 

What you bring

• Strong experience in data engineering, including the design and implementation of scalable data pipelines and data models.
• Ability to operate effectively in an Agentic AI context, contributing to the design, application, and responsible use of AI agents within complex business processes.
• Proficiency in SQL and at least one programming language commonly used in data engineering (e.g. Python).
• Experience working with modern data platforms and cloud‑based data technologies (e.g. data warehouses, data lakes, distributed processing frameworks).
• Solid understanding of data quality, data governance, and data lifecycle management in enterprise environments.
• Ability to translate business and analytical needs into robust technical data solutions.
• Strong collaboration and communication skills, with the ability to work effectively across technical and non‑technical teams.
• Design and build scalable ETL/ELT pipelines, robust data models, and integrated enterprise data systems using modern lakehouse architectures (Parquet, Delta Lake) and platforms such as SAP HANA, Databricks, and big data frameworks (PySpark).
• Develop and manage batch and real-time data pipelines leveraging workflow orchestration tools (e.g., Airflow) and streaming technologies such as Kafka to ensure reliable, efficient, and high-performance data processing.
• Ensure data quality, governance, compliance, and PII protection while implementing CI/CD pipelines and applying best practices with Docker and Git within data engineering workflows.

 

Where you belong