Senior Enterprise Architect: Cloud, Data & AI Strategy

Persistent Systems

Scottsdale (AZ)

Hybrid

USD 160,000 - 230,000

Full time

5 days ago

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Benefits offered by this job

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Job summary

Persistent Systems in Scottsdale, AZ seeks an Enterprise Architect to define and drive the technology strategy across data platforms, cloud, and AI. Lead governance, reference architectures, and modernization initiatives.

The role requires 12–16 years of experience and deep expertise in Databricks, Snowflake, Hadoop, and cloud services. You will architect enterprise data platforms, data governance, and API-led integrations, while mentoring architects and collaborating with executives to maximize

Qualifications

12 to 16+ years of experience in enterprise architecture, data architecture, cloud architecture, or technology leadership.
Experience designing enterprise-scale data platforms and cloud-native solutions.
Deep knowledge of Databricks Lakehouse, Snowflake, Cloudera, Hadoop ecosystems.
Strong experience with API management, microservices, event-driven architecture, and integration patterns.
Hands-on with DevOps, CI/CD pipelines, GitHub, GitLab, Jenkins, Docker, and Kubernetes.

Responsibilities

Define enterprise-wide architecture standards, reference architectures, technology blueprints, and roadmap strategies.
Lead architecture governance, solution reviews, and enterprise technology decision-making processes.
Design scalable, resilient, secure, and cost-optimized enterprise platforms.
Align technology investments and architecture strategies with business goals and digital transformation initiatives.
Drive cloud adoption, platform modernization, and enterprise engineering programs.
Establish technology standards, best practices, and architectural governance frameworks.
Architect enterprise data platforms using Databricks, Snowflake, Hadoop, Cloudera, and cloud-native services.
Design modern Lakehouse, Data Warehouse, Data Lake, Data Mesh, and Data Fabric architectures.
Define enterprise data governance, metadata management, lineage, cataloging, and security frameworks.
Establish data quality, observability, compliance, and monitoring standards.
Design data management strategies for structured, semi-structured, and unstructured data.
Drive enterprise data modernization initiatives and platform consolidation efforts.
Architect large-scale ETL and ELT frameworks supporting batch and real-time processing workloads.
Design and oversee scalable data pipelines across diverse enterprise data sources.
Define data integration patterns using Spark, PySpark, Hadoop ecosystem technologies, and cloud-native services.
Establish best practices for data ingestion, orchestration, transformation, and delivery.
Optimize scalability, performance, reliability, and cost efficiency of enterprise data platforms.
Drive adoption of modern data engineering practices and automation frameworks.
Design enterprise cloud solutions across AWS, Microsoft Azure, and Google Cloud Platform.
Lead cloud migration and application modernization initiatives.
Architect highly available, scalable, resilient, and secure cloud-native platforms.
Define Infrastructure as Code (IaC), DevOps, CI/CD, and automation strategies.
Establish cloud governance, security, networking, compliance, and FinOps best practices.
Define enterprise AI and Machine Learning architecture strategies.
Architect MLOps frameworks and model lifecycle management solutions.
Design Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), and LLM integration architectures.
Establish AI governance, security, compliance, monitoring, and responsible AI practices.
Enable predictive analytics, business intelligence, advanced analytics, and AI-powered decision-making platforms.
Guide adoption of emerging AI technologies and enterprise AI transformation initiatives.
Define enterprise integration architecture and API strategies.
Design Microservices, Event‑Driven, and API-First architectures.
Establish integration patterns leveraging REST APIs, GraphQL, messaging systems, and streaming platforms.
Ensure secure, scalable, reusable, and governed enterprise integrations.
Drive modernization of legacy integration ecosystems.
Define enterprise testing strategies across data, cloud, API, and application ecosystems.
Architect automated testing frameworks for ETL, Data Quality, APIs, AI/ML platforms, and cloud solutions.
Drive adoption of CI/CD-integrated quality engineering practices.
Establish reliability engineering, observability, resiliency testing, and performance testing standards.
Promote automation-driven quality assurance and operational excellence.
Act as a trusted technology advisor to executive leadership and key business stakeholders.
Lead architecture review boards, governance councils, and technical strategy forums.
Mentor solution architects, technical leads, engineers, and data architects.
Drive innovation initiatives and evaluation of emerging technologies.
Build strong partnerships with business, engineering, product, and customer teams.
Influence enterprise technology direction and long‑term digital transformation strategies.