Data Engineering Manager

Job Overview

Data architecture, modeling & reuse

• Own the target data architecture and a governed medallion architecture (Bronze → Silver → Gold) on Databricks / Microsoft Fabric — domains, entities, lineage, and access models that turn raw source data into AI-ready datasets.
• Design for breadth of sources: SAP S/4HANA, 80+ operational systems, Windchill (PLM), SharePoint, TEBIT (40K+ records), and OT / industrial edge data.
• Define reusable data models, taxonomies, and semantic structures — with standards for metadata and business definitions — and promote reuse of shared data products across functions and regions.

MCP & enterprise integration

• Build and operate the central MCP layer — the “API for AI” — so authentication, security, chunking, and format handling are solved once, centrally, not re-solved per solution.
• Define a prioritized MCP connector roadmap (SAP, Windchill, MES…) aligned to value-stream demand, delivering the seven Context 360 types (Role, Process, Asset, Policy, Problem, Objective, Glossary) to agents.

Data availability & AI enablement

• Enable priority AI use cases across the value streams by improving the availability, accessibility, and readiness of critical datasets — with OneData, TEIS, and business teams.
• Remove data bottlenecks (fragmentation, inconsistent structures, poor quality, unclear ownership) so AI teams get the right data at the right quality and governance.

What your background should look like:

• 10+ years in data architecture, data engineering, data platform, or enterprise integration, with 3+ years leading technical teams.
• Proven delivery of a lakehouse / medallion platform on Databricks and/or Microsoft Fabric.
• Enterprise data architecture and information modeling — domains, master/reference data, metadata, taxonomies, and reusable models.
• Hands-on integration of enterprise systems (SAP, Windchill/PLM, MES, content stores) with strong data governance, lineage, and access control.
• Working knowledge of AI/GenAI data needs (RAG, vector indexing/embeddings) and stakeholder management in a matrix organization.

Competencies