Lead Data & Cloud Architect — AI/Analytics, Remote

Lam Research; Fremont (CA); Hybrid; USD 137,000 - 287,000; Full time

Job summary

Lam Research seeks a Lead Data and Cloud Architect in the GBIS group to drive the data architecture and engineering for the Digital Transformation program within Customer Support and Service.

You will own the end-to-end design of the future-state data landscape—models, pipelines, and integration patterns unifying service, installed-base, field operations, and customer engagement data into trusted, analytics-ready data products.

Qualifications

• Extensive experience in modern data warehousing on cloud platforms.
• 5+ years Spark and Python for big data processing.
• 3+ years implementing end-to-end medallion architecture using Databricks or similar.
• 8+ years of SQL in cloud-native data warehousing with data modeling.
• Experience with SAP/MS Dynamics and IoT/telemetry data.
• Strong communication and ability to influence executives with insights.

Responsibilities

• Define and document target-state data architecture for Customer Support and Service, with source-to-target mappings and canonical models.
• Author detailed technical specifications and guide implementation to delivery.
• Architect scalable, secure data pipelines and integrations across enterprise systems.
• Serve as a technical advisor on analytics design for cross-functional solutions.
• Ensure solutions align with Data COE governance and long-term data strategy.
• Design Analytics and AI applications using Azure stack and Power BI, including ETL processes.
• Collaborate with stakeholders to sequence roadmap and resolve design trade-offs.

Skills

Data engineering

Cloud data architecture

Big data processing

Stakeholder communication

Tools

Azure Synapse

Databricks

Power BI

SQL

Python

Azure Data Lake

Job description

Lam Research seeks a Lead Data and Cloud Architect in the GBIS group to drive the data architecture and engineering for the Digital Transformation program within Customer Support and Service.

You will own the end-to-end design of the future-state data landscape—models, pipelines, and integration patterns unifying service, installed-base, field operations, and customer engagement data into trusted, analytics-ready data products.