{"id":3991866,"url":"https://alion.io/job/applied-materials-data-solutions-architect-data-platform-data-products-ai-ready-data","title":"Data Solutions Architect - Data Platform, Data Products & AI-Ready Data","company":{"id":3903,"name":"Applied Materials","domain":"appliedmaterials.com","url":"https://alion.io/company/amat","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":87,"open_postings":261,"ghost_share":0.004,"stale_share":0.51,"repost_share":0.011,"time_to_fill_p50_days":54,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":179500,"max":246500,"currency":"USD","period":"year","gross":null,"usd_annual":246500},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CAPA","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"FinOps","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-09T23:40:58Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T23:40:58Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Who We Are\nApplied 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.\nWhat We Offer\nSalary:\n$179,500.00 - $246,500.00Location:\nSanta Clara,CAYou’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.\nAt 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.\nAs 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:\nArchitect 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.\nProvide 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.\nProvide architecture leadership for ongoing Data Platform production support and operations, ensuring platform availability, reliability, scalability, performance, security and operational sustainability.\nProvide and/or oversee platform and data services support to meet agreed availability, performance, service-level agreements, data freshness, reliability and customer satisfaction targets.\nLead 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.\nLead 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.\nEstablish 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.\nEnsure new capabilities are designed for Day-2 production operations, including supportability, monitoring, automation, recovery, operational documentation, runbooks and production readiness.\nArchitect reusable solutions for multi-tenant, shared and dedicated data environments, including appropriate isolation across identity, data, compute, storage and network boundaries.\nEstablish 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.\nDefine 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.\nLead 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.\nDefine 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.\nArchitect 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.\nDefine reusable reference architectures, implementation patterns, engineering standards, Architecture Decision Records and golden paths that can be consistently adopted across enterprise data initiatives.\nDrive 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.\nDrive DataOps, DevSecOps and FinOps practices, including CI/CD, Infrastructure as Code, automated testing, deployment automation, security validation and cloud/platform cost optimization.\nIdentify recurring operational issues, technology limitations and technical debt and convert them into prioritized platform modernization and continuous-improvement initiatives.\nPlan 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.\nEnsure 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.\nCollaborate 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.\nMentor architects, technical leads and engineers and improve technical maturity through architecture reviews, reusable patterns, technical documentation and engineering best practices.\nDeliver project, architecture and production-support services within the area of responsibility while optimizing resource utilization, infrastructure consumption and overall platform cost.\nFunctional Knowledge:\nDemonstrates significant depth and breadth of expertise in enterprise data architecture, cloud data platforms, Data Lakes, Lakehouse architecture, distributed data processing and modern data engineering.\nDemonstrates advanced knowledge of technologies such as Databricks, Apache Spark, Delta Lake, Apache Iceberg, SQL, Python, cloud object storage and distributed computing architectures.\nPossesses 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.\nDemonstrates expertise in Data Products, Data Mesh concepts, metadata management, data contracts, semantic models, business glossaries, ontologies and Knowledge Graph architectures.\nUnderstands modern AI/ML and GenAI data architecture, including RAG, vector search, semantic retrieval, Text-to-SQL, AI-ready datasets and agentic AI consumption patterns.\nPossesses strong knowledge of data governance, security, privacy, lineage, data quality, observability, RBAC, policy-based access, encryption and multi-tenant isolation.\nDemonstrates knowledge of DataOps, DevSecOps, Infrastructure as Code, CI/CD, production operations, Site Reliability Engineering and observability practices, disaster recovery and FinOps.\nMaintains awareness of evolving industry practices and technologies and evaluates their applicability to enterprise data-platform strategy.\nBusiness Expertise:\nUnderstands complex enterprise data and analytics requirements and translates business objectives into scalable, secure, governed and operationally sustainable technical architectures.\nInterprets internal and external business and technology challenges and recommends architecture patterns and industry best practices to improve platform capabilities, Data Products, operational processes and services.\nUnderstands the importance of managing data as a reusable enterprise asset and product, balancing domain-specific requirements with interoperability, governance and enterprise reuse.\nEvaluates architecture decisions considering business value, customer requirements, security, performance, operational complexity, scalability, technical debt, time-to-market and total cost of ownership.\nConnects current operational requirements with long-term platform strategy to ensure near-term delivery decisions do not compromise future scalability, maintainability or interoperability.\nLeadership\nProvides senior technical and architecture leadership across enterprise Data Platform and Global Data Office initiatives, influencing multiple engineering, platform, application and infrastructure teams.\nLeads architecture and design reviews and drives technical decisions for complex solutions spanning multiple organizations and technology domains.\nProvides technical leadership during critical production incidents, RCA, platform stabilization, performance issues and major production changes.\nLeads medium-to-large architecture and technical initiatives with significant technical dependencies, resource requirements, risk and complexity.\nMentors architects, technical leads and engineers and promotes adoption of common architecture standards, reusable components and engineering best practices.\nInfluences stakeholders and technology teams without relying on direct organizational authority and builds alignment around common enterprise architecture and platform objectives.\nProblem Solving\nLeads teams in solving highly complex data-platform, architecture, performance, scalability, security and production-operational problems spanning multiple technologies and organizational boundaries.\nUses sophisticated analytical and systems thinking to identify root causes, evaluate architectural trade-offs and develop innovative and sustainable solutions.\nDiagnoses recurring production issues and distinguishes operational symptoms from structural issues requiring architecture or engineering redesign.\nEvaluates alternative technologies and architectural approaches based on performance, scalability, reliability, security, maintainability, interoperability, operational complexity and cost.\nAnticipates future platform scalability and operational challenges and proactively designs capabilities and controls to address them.\nImpact\nDirectly impacts the availability, scalability, security, reliability, performance and continued evolution of enterprise data platforms.\nInfluences enterprise architecture decisions affecting Data Platforms, Data Products, analytics, AI/ML, GenAI and enterprise data consumption.\nImproves production stability and operational effectiveness through observability, automation, RCA, architecture modernization and technical-debt reduc...","description_format":"text","description_chars":14997,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Information Security","Semiconductor Equipment"],"lifecycle":[{"event":"open","at":"2026-11-09T23:40:58Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 528 · green card filings: 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