Arkhya Tech. Inc.

Enterprise Architect – Data, Cloud, AI & Integration

Contract · In Office · Scottsdale, Arizona (USA)

Posted Sep 14, 2026

Work Options

In Office

Cloud Stack

Oracle GCP Azure AWS Snowflake

Skills

Kafka Docker Spark Kubernetes SQL Hadoop

Industry

Services AI Machine Learning Consulting

Job Type

Contract

Position Group

Other Positions

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Enterprise Architect – Data, Cloud, AI & Integration
Location: Scottsdale, AZ
Work Arrangement: Onsite
Role Overview
We are seeking a highly experienced Enterprise Architect to define, shape, and drive enterprise technology strategy across Data Platforms, Cloud, AI/ML, Analytics, Integration, and Digital Transformation.
The ideal candidate will have extensive experience designing and governing enterprise-scale architectures using technologies such as Databricks, Snowflake, Hadoop, AWS, Azure, GCP, Apache Spark, Kafka, APIs, Kubernetes, DevOps, and AI/ML platforms.
This role will serve as a strategic technology leader and trusted advisor, partnering with executive leadership, business stakeholders, engineering organizations, and customers to develop scalable, secure, resilient, cost-effective, and future-ready technology platforms that deliver measurable business value.
Key Responsibilities
Enterprise Architecture & Technology Strategy
Define enterprise architecture standards, principles, reference architectures, and technology roadmaps.
Lead architecture governance, solution architecture reviews, and technology selection decisions.
Design scalable, resilient, secure, highly available, and cost-optimized enterprise solutions.
Align technology strategy and architecture with business objectives and digital transformation initiatives.
Drive enterprise cloud adoption, modernization, platform engineering, and technology transformation programs.
Establish architecture governance processes, standards, and best practices across technology domains.
Data Platform Architecture
Architect enterprise-scale data platforms leveraging Databricks, Snowflake, Hadoop, and cloud-native data services.
Design and implement modern Lakehouse, Data Warehouse, Data Lake, Data Mesh, and Data Fabric architectures.
Define enterprise data governance, metadata management, data lineage, security, privacy, and compliance frameworks.
Establish standards for data quality, observability, monitoring, reliability, and operational governance.
Develop strategies for managing structured, semi-structured, and unstructured enterprise data.
Define scalable architectures for analytics, reporting, business intelligence, and advanced data use cases.
Data Engineering & ETL/ELT
Architect enterprise-scale ETL/ELT frameworks supporting batch, streaming, and real-time processing.
Design scalable data pipelines integrating data from diverse enterprise source systems.
Define data integration patterns using Apache Spark, PySpark, Databricks, Hadoop, Kafka, and cloud-native services.
Establish best practices for data ingestion, transformation, orchestration, processing, and data delivery.
Optimize data pipelines for performance, scalability, reliability, and cost efficiency.
Establish standards for pipeline monitoring, data quality, error handling, and operational support.
Cloud Architecture
Design and govern enterprise solutions across AWS, Microsoft Azure, and Google Cloud Platform (GCP).
Lead cloud migration, modernization, and hybrid/multi-cloud initiatives.
Architect secure, highly available, scalable, and resilient cloud platforms.
Define Infrastructure as Code (IaC), CI/CD, DevOps, and platform engineering strategies.
Establish cloud governance, security, networking, identity, compliance, and operational standards.
Drive FinOps and cloud cost-optimization initiatives.
AI/ML & Advanced Analytics
Define enterprise-wide AI/ML architecture and technology strategy.
Architect MLOps platforms and end-to-end model development, deployment, monitoring, and lifecycle management.
Design enterprise solutions incorporating Generative AI, LLMs, Agentic AI, RAG, and vector databases.
Define architectures for integrating AI capabilities into enterprise applications and data platforms.
Establish AI governance, security, monitoring, responsible AI, and model-risk management practices.
Enable predictive analytics, machine learning, business intelligence, and advanced analytics capabilities.
API & Integration Architecture
Define enterprise integration strategy, API standards, and integration reference architectures.
Design API-first, microservices, event-driven, and distributed architectures.
Establish integration patterns using REST APIs, GraphQL, messaging platforms, streaming technologies, and ESB solutions.
Define secure, scalable, reusable, and highly available enterprise integration frameworks.
Drive modernization of legacy integration platforms and services.
Quality Engineering & Test Automation
Define enterprise-wide quality engineering and testing strategies across data, cloud, APIs, applications, and AI/ML platforms.
Architect automated testing frameworks for ETL/ELT pipelines, data quality, APIs, AI/ML solutions, and cloud platforms.
Integrate automated testing and quality gates into CI/CD pipelines.
Establish standards for performance, reliability, resilience, observability, and security testing.
Promote shift-left testing and continuous quality engineering practices.
Leadership & Stakeholder Management
Serve as a trusted technology advisor to executive leadership, business stakeholders, customers, and engineering organizations.
Lead architecture review boards, technical governance forums, and strategic technology discussions.
Mentor architects, engineers, technical leads, and development teams.
Communicate complex architectural concepts and technology strategies effectively to both technical and executive audiences.
Drive innovation, technology adoption, and continuous improvement across the enterprise.
Partner with customers and business leaders to translate business challenges into practical technology solutions.
Required Technical Expertise
Data & Analytics
Databricks Lakehouse
Snowflake
Hadoop Ecosystem — HDFS, Hive, Spark, YARN
Delta Lake
Data Lake, Data Warehouse, Lakehouse
Data Mesh and Data Fabric
Data Governance, Quality, Lineage, and Observability
Data Engineering
ETL/ELT Architecture
Enterprise Data Pipelines
Apache Spark / PySpark
Kafka
Batch and Real-Time Streaming
Data Integration and Orchestration
Data Quality and Observability
Cloud
AWS
Microsoft Azure
Google Cloud Platform (GCP)
Cloud Security and Networking
Infrastructure as Code
Terraform
CloudFormation
CI/CD and DevOps
Cloud Governance and FinOps
AI/ML
Machine Learning Platforms
MLOps
Generative AI
Large Language Models (LLMs)
Agentic AI
Retrieval-Augmented Generation (RAG)
Vector Databases
AI Governance and Responsible AI
API & Enterprise Integration
REST APIs
GraphQL
Enterprise Service Bus (ESB)
Event-Driven Architecture
Microservices
Messaging and Streaming
API Management and Integration Patterns
DevOps & Automation
CI/CD Pipelines
GitHub / GitLab
Jenkins
Docker
Kubernetes
Infrastructure as Code
Automated Testing and Quality Engineering
Databases
Snowflake
SQL Server
PostgreSQL
Oracle
NoSQL Databases
Relational and Distributed Data Platforms
Required Leadership & Professional Skills
Executive-level stakeholder management
Enterprise architecture leadership
Strategic technology planning
Strong business and technology alignment
Architecture governance and decision-making
Customer-facing consulting experience
Strong communication and presentation skills
Technical mentoring and team leadership
Problem-solving and analytical thinking
Ability to influence across organizational boundaries
Strong understanding of emerging technologies and industry trends
Ideal Candidate Profile
The ideal candidate is a strategic, hands-on Enterprise Architect who can operate effectively across business and technology domains. They should possess the ability to translate business objectives into enterprise architecture strategies while providing sufficient technical depth to guide engineering teams through complex implementations.
This role is particularly suited to an architect with a strong track record of delivering and modernizing enterprise-scale Data, Cloud, AI, Analytics, Integration, and Digital platforms, while establishing the governance, security, scalability, and operational practices required for long-term success.