Lead Data Engineer

• Own end-to-end architecture for ACDP: data warehouses, tenant data stores, lakes, and feature stores supporting analytics, ML, and AI agent use cases.

• Design and build the tenant registry and tenant data warehouse layer that underpins multi-tenant AI Agent workloads.

• Build and govern the Data Agent layer that supplies on-prem/customer-controlled data to cloud LLM reasoning in the Hybrid AI zone, with staged, explainable data outputs and human review checkpoints.

• Define data classification and routing rules that determine which data can flow to Cloud AI, which requires the Hybrid AI path, and which must remain fully on-prem/deterministic — protecting customer IP and production-critical logic.

• Set engineering standards for ETL/ELT design, orchestration, and data quality across the team.

• Lead design and code reviews; mentor data engineers and act as technical escalation point.

• Drive automation and reliability of pipelines, including SLAs, observability, and incident response.

• Own data security, access control, lineage, and governance compliance at the platform level, across cloud, hybrid, and on-prem tiers.

• Partner with data scientists and analysts to ensure platform data is ML-ready and supports predictive modeling.

• Build and maintain the data/event pipelines connecting ERP source systems (e.g., EnCore) to the EnCore Agent Hub, supporting secure read/write and event-and-trigger flows.

• Guide GenAI/LLM data enablement, including embeddings, vector database design, and RAG pipeline architecture, in support of Amtech AI Cloud agent orchestration.

• Represent data engineering in cross-functional architecture (ARB) and platform roadmap discussions.

• Optimize pipelines for low-latency access; establish partitioning, caching, and indexing strategies as team standards.