Data Architect - Enterprise Analytics

Lam Research•Fremont, CA

1d•$137,000 - $287,000•Hybrid

About The Position

As a Data Architect in Global Information Systems, you will design and deliver scalable, governed, and AI-ready enterprise data solutions supporting analytics, digital transformation, and emerging AI use cases. You will provide technical leadership across data architecture, modeling, integration, engineering, semantic modeling, and data product design, translating complex business requirements into trusted, reusable enterprise data capabilities. You will define enterprise architecture and standards, design data models and modern cloud data platforms, establish patterns for data integration and engineering, and create governed data foundations that are secure, discoverable, and optimized for downstream use. You will partner with business and technical teams, lead architecture reviews, provide hands-on technical guidance, and evaluate emerging technologies. The role may use a hybrid work-location model, depending on the needs of the role. Lam describes On-site Flex as working at least three days per week onsite and Virtual Flex as working one to two days per week onsite, with remote work for the balance. The specific model for this position is not stated. The stated California San Francisco Bay Area salary range is $137,000 to $287,000; offers depend on factors including work location, level, education, training, skills, experience, and internal comparisons. Lam states that it offers a comprehensive set of benefits, but the posting does not specify individual benefits.

Requirements

10 or more years of experience in data architecture, data engineering, enterprise data platforms, data warehousing, or related technology roles.
Deep expertise in enterprise data modeling and a strong understanding of modern data architecture principles and design patterns.
Hands-on technical skills with SQL, Python/PySpark, Spark, ETL/ELT, APIs, and distributed data processing.
Strong experience with modern cloud data platforms such as Microsoft Fabric, Databricks, Azure, Snowflake, or equivalent technologies.
Proven experience architecting and delivering large-scale enterprise data solutions from source through consumption.
Experience integrating complex enterprise platforms and data sources such as SAP, MES, PLM, IoT, APIs, files, and operational databases.
Strong understanding of data governance, quality, metadata, lineage, security, and access management.
Ability to balance strategic architecture with hands-on technical problem-solving and work effectively with architects, engineers, application teams, and business stakeholders.
Strong communication skills and ability to explain complex data architecture concepts to technical and business audiences.
Self-starter and continuous learner who can thrive in a fast-paced, dynamic environment, take end-to-end ownership, and solve complex data challenges.

Nice To Haves

Experience with manufacturing, semiconductor, engineering, equipment, IoT, or other complex operational data environments.
Experience designing enterprise semantic models, reusable data products, and common data domains.
Understanding of how enterprise data architecture and semantic/contextual data models enable AI-ready data consumption.
Experience with real-time or streaming architectures, event-driven integration, and large-scale data processing.
Experience with CI/CD, automated testing, monitoring, and production engineering practices for enterprise data solutions.
Bachelor’s degree in Computer Science, Information Technology, Data Engineering, Data Science, Engineering, or a related discipline; a master’s degree is preferred.

Responsibilities

Define and evolve enterprise data architecture, reference architectures, standards, patterns, and technology roadmaps.
Design conceptual, logical, physical, dimensional, canonical, and semantic data models for complex business entities, relationships, and processes.
Architect scalable data integration and ingestion patterns using batch, near-real-time, streaming, APIs, and event-driven approaches.
Design modern lakehouse, data warehouse, medallion, semantic-layer, and data-product architectures using platforms such as Microsoft Fabric, Databricks, and Azure.
Create AI-ready data foundations by ensuring enterprise data is modeled, contextualized, governed, discoverable, secure, and optimized for downstream consumption.
Define reusable architecture and engineering patterns for data ingestion, transformation, storage, integration, and delivery.
Establish standards for data quality, metadata, lineage, security, access control, observability, performance, and lifecycle management.
Partner with business SMEs, application teams, data engineers, analytics teams, and solution architects to translate business needs into scalable technical solutions.
Lead architecture and design reviews, provide hands-on technical guidance, and help engineering teams resolve complex data and integration challenges.
Evaluate emerging data technologies and architecture patterns and recommend solutions aligned with enterprise strategy.