Data Solutions Architect - Data Platform, Data Products & AI-Ready Data
Applied Materials3 days ago
Base Salary
$180k - $247k/yr
Responsibilities
- Architect enterprise-scale Data Platform, Data Lake, Lakehouse, data integration, processing, storage, transformation, serving, analytics, and downstream consumption solutions.
- Provide architecture and technical leadership for cloud data platforms, production operations, availability, reliability, scalability, performance, security, and service-level objectives.
- Lead resolution of complex production issues, root cause analyses, corrective actions, architecture improvements, automation, and platform standards.
- Establish standards for monitoring, observability, alerting, data quality, data freshness, disaster recovery, backup and recovery, capacity planning, and platform health.
- Architect secure multi-tenant data environments with identity and access controls, metadata catalogs, classification, lineage, auditability, encryption, retention, and secure data sharing.
- Define enterprise standards for Data Products, data contracts, semantic layers, business glossaries, ontologies, knowledge graphs, metadata, and reusable semantic models.
- Architect AI-ready data foundations for AI/ML, GenAI, RAG, vector and semantic search, Text-to-SQL, and agentic AI solutions.
- Create reference architectures, implementation patterns, engineering standards, Architecture Decision Records, and golden paths.
- Drive DataOps, DevSecOps, FinOps, infrastructure automation, automated testing, deployment automation, security validation, and cloud cost optimization.
- Lead medium-to-large data-platform initiatives, architecture reviews, governance, modernization, technical-debt reduction, and cross-functional delivery.
- 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 promote reusable architecture patterns and engineering best practices.
Requirements
- Significant depth and breadth 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.
- Expertise in Data Products, Data Mesh concepts, metadata management, data contracts, semantic models, business glossaries, ontologies, and Knowledge Graph architectures.
- Knowledge 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 translate complex enterprise data and analytics requirements into scalable, secure, governed, and operationally sustainable architectures.
- Ability to lead complex architecture decisions, production incidents, modernization initiatives, and cross-organizational technical programs.
- Ability to communicate with executives, business stakeholders, architects, engineers, and operations teams and influence without direct authority.
Benefits
- Comprehensive benefits package and potential eligibility for bonus and stock award programs, as applicable.
- Full-time, regular employee position in Santa Clara, California.
- Relocation eligible.
- No travel required.
Tech Stack
Categories
Data Engineering
About Applied Materials
Applied Materials builds semiconductor and display manufacturing equipment and related materials engineering technologies used by chip and flat-panel makers. It sells capital tools, process technology, and lifecycle services and software to support high-volume wafer fabrication and display production worldwide. Founded in 1967 and headquartered in Santa Clara, California, the company is publicly traded on NASDAQ under the ticker AMAT.