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

Applied Materials • Santa Clara, CA

1d • $179,500 - $246,500

About The Position

Applied Materials is seeking a Data Solutions Architect to provide enterprise architecture and technical leadership for its Data Platform, Data Products, and AI-ready data capabilities. The role covers cloud data platforms, data lakes and lakehouses, data integration, batch and streaming processing, storage, transformation, serving, analytics, and downstream consumption. The architect will translate cross-functional business and technical needs into scalable, secure, governed, and operationally sustainable architectures, while balancing performance, reliability, interoperability, operational complexity, and cost.

The position includes leadership of production operations and complex incident resolution, including root cause analysis and the translation of findings into architecture improvements, engineering work, automation, and platform standards. It establishes operating practices for observability, data quality, freshness, reliability, capacity, recovery, security, governance, and production readiness. It also defines reusable architecture patterns for multi-tenant environments, Data Products, semantic layers, metadata and knowledge graphs, and AI/ML and GenAI use cases such as RAG, vector and semantic search, Text-to-SQL, and agentic AI.

The architect will guide platform modernization and adoption of technologies such as Databricks, Apache Spark, Delta Lake, Apache Iceberg, Kafka, cloud object storage, metadata/catalog platforms, and enterprise BI technologies. The role drives DataOps, DevSecOps, and FinOps practices and leads complex initiatives through architecture governance and organizational controls. It collaborates across engineering, infrastructure, cybersecurity, governance, AI/ML, applications, and business teams, and mentors architects, technical leads, and engineers.

Requirements

• Significant depth and breadth of expertise in enterprise data architecture, cloud data platforms, data lakes, lakehouse architecture, distributed data processing, and modern data engineering.
• Advanced knowledge of Databricks, Apache Spark, Delta Lake, Apache Iceberg, SQL, Python, cloud object storage, and distributed computing architectures.
• Strong understanding of batch, streaming, event-driven, and near-real-time architectures, including Kafka, Spark Structured Streaming, and Change Data Capture patterns.
• Expertise in Data Products, Data Mesh concepts, metadata management, data contracts, semantic models, business glossaries, ontologies, and Knowledge Graph architectures.
• Understanding of modern AI/ML and GenAI data architecture, including RAG, vector search, semantic retrieval, Text-to-SQL, AI-ready datasets, and agentic AI consumption patterns.
• Strong knowledge of data governance, security, privacy, lineage, data quality, observability, RBAC, policy-based access, encryption, and multi-tenant isolation.
• Knowledge of DataOps, DevSecOps, Infrastructure as Code, CI/CD, production operations, Site Reliability Engineering, observability, disaster recovery, and FinOps.
• Ability to understand complex enterprise data and analytics requirements and translate business objectives into scalable, secure, governed, operationally sustainable architectures.
• Ability to evaluate architecture decisions based on business value, customer requirements, security, performance, operational complexity, scalability, technical debt, time-to-market, and total cost of ownership.
• Ability to lead architecture and design reviews, make technical decisions across organizations, and provide technical leadership during critical incidents, platform stabilization, performance issues, and production changes.
• Ability to solve highly complex data-platform, architecture, performance, scalability, security, and production-operational problems across technologies and organizational boundaries.
• Strong analytical and systems-thinking skills to identify root causes, evaluate architectural trade-offs, and develop sustainable solutions.
• Ability to communicate complex architecture and technology concepts effectively to executives, business stakeholders, architects, engineers, and operations teams.
• Ability to facilitate discussions with competing requirements, negotiate across organizations, influence stakeholders without direct authority, and build collaborative partnerships.
• Ability to mentor technical teams and promote common architecture standards, reusable components, and engineering best practices.

Responsibilities

• Architect and design complex enterprise-scale data platform, data lake, and lakehouse solutions, translating architecture into functional and technical specifications.
• Provide end-to-end architecture and technical leadership across cloud data platforms, data integration, batch and streaming processing, storage, transformation, serving, analytics, and downstream consumption.
• Lead architecture for data platform production support and operations, ensuring availability, reliability, scalability, performance, security, and operational sustainability.
• Provide or oversee platform and data services support to meet availability, performance, service-level, freshness, reliability, and customer satisfaction targets.
• Lead resolution of complex cross-platform production issues and coordinate with engineering and infrastructure teams.
• Lead technical root cause analysis for high-impact and recurring incidents and ensure corrective and preventive actions become architecture improvements, engineering backlog items, automation, and platform standards.
• Establish standards for monitoring, observability, alerting, data quality and freshness, ingestion latency, pipeline reliability, capacity planning, performance optimization, disaster recovery, backup and recovery, and platform health.
• Ensure new capabilities are designed for Day-2 operations, including supportability, monitoring, automation, recovery, documentation, runbooks, and production readiness.
• Architect reusable solutions for multi-tenant, shared, and dedicated data environments, with appropriate identity, data, compute, storage, and network isolation.
• Establish security and governance architecture patterns for identity and access management, RBAC, policy-based access, metadata catalogs, 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 contracts, ownership, quality, metadata, lineage, discoverability, interoperability, versioning, and service-level expectations.
• Lead architecture for enterprise semantic layers and reusable semantic models that provide consistent business meaning across analytics, applications, and AI use cases.
• Define and evolve capabilities for business glossaries, metadata, ontologies, and Knowledge Graphs linking business concepts, Data Products, datasets, applications, and enterprise domains.
• Architect AI-ready data foundations for AI/ML, GenAI, 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 for enterprise data initiatives.
• Drive architecture modernization and adoption of appropriate data platform technologies while considering 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 cost optimization.
• Identify recurring operational issues, technology limitations, and technical debt and turn them into prioritized modernization and continuous-improvement initiatives.
• Plan and provide technical leadership for medium-to-large, complex, integrated data-platform initiatives within agreed scope, timeline, budget, architecture, security, and quality guardrails.
• Ensure adherence to organizational architecture governance, project management, development, testing, cybersecurity, service management, change and release management, incident and problem management, and RCA processes.
• Collaborate with Data Engineering, Platform Engineering, Cloud Infrastructure, Networking, Cybersecurity, Data Governance, Enterprise Architecture, AI/ML, application teams, and business stakeholders.
• Mentor architects, technical leads, and engineers and improve technical maturity through reviews, reusable patterns, documentation, and engineering best practices.
• Deliver project, architecture, and production-support services while optimizing resource utilization, infrastructure consumption, and platform cost.

Benefits

• Comprehensive benefits package
• Potential eligibility for a bonus program
• Potential eligibility for a stock award program
• Relocation eligible

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed