Data Solutions Architect - Data Platform, Data Products & AI-Ready Data

Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.

What We Offer

Salary:

$179,500.00 - $246,500.00

Location:

Santa Clara,CA

You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

As a Software Engineer at Applied Materials, you’ll dive deep into ground-breaking technologies—like machine learning and AI—to craft novel software solutions that solve our customers’ high-value problems. Our Software Engineers are responsible for designing, prototyping, developing, and debugging software solutions for semiconductor equipment components and devices to ensure quality and functionality. You'll develop software documentation and test procedures, troubleshoot software problems, and communicate with internal customers to understand project requirements. As part of our team, you'll contribute your expertise in intricate systems, deciphering code, and anticipating software behaviors to ensure Applied remains the leader in the semiconductor and display sectors.

Key Responsibilities:

• •Architect and design complex, enterprise-scale Data Platform, Data Lake and Lakehouse solutions using multiple technologies to address cross-functional business and technical needs, and translate architecture into functional and technical specifications.
• •Provide end-to-end architecture and technical leadership across cloud data platforms, Lakehouse, Data Lake, data integration, batch and streaming processing, data storage, transformation, serving, analytics and downstream consumption .
• •Provide architecture leadership for ongoing Data Platform production support and operations , ensuring platform availability, reliability, scalability, performance, security and operational sustainability.
• •Provide and/or oversee platform and data services support to meet agreed availability, performance, service-level agreements, data freshness, reliability and customer satisfaction targets .
• •Lead resolution of complex cross-platform production issues involving Databricks, Spark, cloud infrastructure, storage, streaming, data pipelines, networking, security, data quality and downstream consumers , partnering with appropriate engineering and infrastructure teams.
• •Lead technical Root Cause Analysis (RCA) for high-impact and recurring incidents and ensure corrective and preventive actions are translated into architecture improvements, engineering backlogs, automation and platform standards.
• •Establish architecture and operating standards for monitoring, observability, alerting, data quality, data freshness, ingestion latency, pipeline reliability, capacity planning, performance optimization, disaster recovery, backup/recovery and platform health .
• •Ensure new capabilities are designed for Day-2 production operations , including supportability, monitoring, automation, recovery, operational documentation, runbooks and production readiness.
• •Architect reusable solutions for multi-tenant, shared and dedicated data environments , including appropriate isolation across identity, data, compute, storage and network boundaries.
• •Establish architecture patterns for security and governance , including identity and access management, RBAC and policy-based access, metadata catalogs, data classification, lineage, auditability, encryption, customer-managed keys/BYOK, retention and secure data sharing.
• •Define architecture and technical standards for Data Products and Data-as-a-Product , including data contracts, ownership, quality, metadata, lineage, discoverability, interoperability, versioning and service-level expectations.
• •Lead architecture for enterprise semantic layers , including common business entities, dimensions, measures, metrics, KPIs, relationships and reusable semantic models providing consistent business meaning across analytics, applications and AI use cases.
• •Define and evolve enterprise capabilities for business glossaries, metadata, ontologies and Knowledge Graphs , enabling relationships across business concepts, Data Products, physical datasets, applications and enterprise domains.
• •Architect AI-ready data foundations supporting AI/ML, GenAI, Retrieval-Augmented Generation (RAG), vector and semantic search, natural-language data discovery, Text-to-SQL and agentic AI solutions using governed enterprise data and semantic context.
• •Define reusable reference architectures, implementation patterns, engineering standards, Architecture Decision Records and golden paths that can be consistently adopted across enterprise data initiatives.
• •Drive architecture modernization and adoption of appropriate technologies such as Databricks, Apache Spark, Delta Lake, Apache Iceberg, Kafka, cloud object storage, metadata/catalog platforms and enterprise BI technologies , while balancing scalability, interoperability, security, operational complexity and cost.
• •Drive DataOps, DevSecOps and FinOps practices, including CI/CD, Infrastructure as Code, automated testing, deployment automation, security validation and cloud/platform cost optimization.
• •Identify recurring operational issues, technology limitations and technical debt and convert them into prioritized platform modernization and continuous-improvement initiatives .
• •Plan and provide technical leadership for medium-to-large, complex and integrated data-platform initiatives to ensure effective execution within agreed scope, timeline, budget, architecture, security and quality guardrails .
• •Ensure adherence to organizational processes and controls for architecture governance, project management, software development, testing, cybersecurity, service management, change management, release management, incident/problem management and RCA .
• •Collaborate with Data Engineering, Platform Engineering, Cloud Infrastructure, Networking, Cybersecurity, Data Governance, Enterprise Architecture, AI/ML, application teams and business stakeholders to deliver integrated enterprise solutions.
• •Mentor architects, technical leads and engineers and improve technical maturity through architecture reviews, reusable patterns, technical documentation and engineering best practices .
• •Deliver project, architecture and production-support services within the area of responsibility while optimizing resource utilization, infrastructure consumption and overall platform cost .

Functional Knowledge:

• •Demonstrates significant depth and breadth of expertise in enterprise data architecture, cloud data platforms, Data Lakes, Lakehouse architecture, distributed data processing and modern data engineering .
• •Demonstrates advanced knowledge of technologies such as Databricks, Apache Spark, Delta Lake, Apache Iceberg, SQL, Python, cloud object storage and distributed computing architectures .
• •Possesses strong understanding of batch, streaming, event-driven and near-real-time architectures , including technologies and patterns such as Kafka, Spark Structured Streaming and Change Data Capture.
• •Demonstrates expertise in Data Product