Forward Deployment Engineer – AI Data Platforms & Data Spaces

Overview

In this role you help clients accelerate their AI transformation by assessing data ecosystems and designing AI-ready data platforms and sovereign data spaces. You will work with customers and cross-functional teams to define roadmaps, build production-ready data solutions, and drive measurable business outcomes. You’ll innovate beyond standard implementations while staying security-conscious and governance-aligned. This is a customer-facing role that combines engineering excellence with strategic consulting to deliver high-value AI-enabled data capabilities.

Responsibilities

• Engage with customers to identify business challenges, data opportunities, and AI use cases
• Assess enterprise data ecosystems, applications, infrastructure, and digital maturity
• Define roadmaps for AI-native transformation, focusing on AI-ready data platforms and sovereign data spaces
• Design, develop, and deploy production-ready data platforms, data spaces, and rapid PoVs
• Architect modern enterprise data solutions (data lakes, lakehouses, data warehouses, federated data spaces)
• Design scalable ETL/ELT pipelines, API-driven integrations, streaming architectures, and AI-ready data layers
• Build data space solutions extending GAIA-X, IDSA, DSIF to fit customer needs
• Recommend technologies and architectures in a technology-agnostic way while leveraging AI-assisted development
• Ensure security-by-design, scalability, maintainability, and governance/compliance alignment
• Collaborate with AI Engineers, Solution Architects, and stakeholders to deliver measurable business outcomes

Key requirements

• 5+ years in a customer-facing role focused on enterprise data platforms (Forward Deployment Engineer, Data Platform Engineer, Data Architect, Solutions Engineer)
• Proven experience designing and implementing enterprise data platforms, AI data layers, and sovereign data spaces
• Deep expertise in data lakes, lakehouses, data warehouses, ETL/ELT, data federation, data virtualization, APIs, event-driven architectures, and distributed data systems
• Strong understanding of AI-ready data architectures supporting analytics, ML, generative AI, RAG, vector databases, and knowledge graphs
• Experience with sovereign data ecosystems and interoperability frameworks (GAIA-X, IDSA, DSIF) and delivering customized solutions
• Broad knowledge of cloud, hybrid, and enterprise technologies with ability to learn new tools quickly
• Experience with containerization, infrastructure automation, CI/CD, DataOps/MLOps, observability, and secure-by-design practices
• Ability to leverage AI-assisted development and intelligent engineering tools to accelerate delivery while ensuring quality and security
• Excellent consulting, communication, stakeholder management, and problem-solving skills

• consulting orientation
• stakeholder management
• problem-solving
• data platforms (data lakes, lakehouses, data warehouses)
• ETL/ELT
• data federation and virtualization

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