Enterprise Architect - Data, Cloud & AI

We are seeking a highly experienced Enterprise Architect to define and drive enterprise technology strategy across Data Platforms, Cloud, AI/ML, Analytics, and Enterprise Integration. The ideal candidate will have strong hands-on architecture experience with Databricks, Cloudera, Snowflake, Hadoop, Data Engineering, and Cloud Platforms, along with the ability to design scalable, secure, resilient, and future-ready enterprise solutions. This role will work closely with executive leadership, business stakeholders, engineering teams, architects, and customers to define enterprise architecture, establish technology standards, drive modernization, and deliver large-scale digital, data, and AI transformation initiatives., Enterprise Architecture & Technology Strategy

• Define enterprise-wide architecture standards, reference architectures, technology principles, and strategic technology roadmaps.
• Lead architecture governance, solution reviews, technology evaluations, and architecture decision processes.
• Design scalable, resilient, secure, highly available, and cost-optimized enterprise platforms.
• Align enterprise technology architecture with business objectives, digital transformation strategies, and long-term organizational goals.
• Drive cloud adoption, legacy modernization, platform engineering, and enterprise transformation initiatives.
• Evaluate emerging technologies and recommend solutions that provide measurable business and operational value.

Data Platform Architecture

• Architect enterprise-scale data platforms using Databricks, Cloudera, 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, and access-control frameworks.
• Establish standards for data quality, observability, monitoring, compliance, and operational reliability.
• Develop strategies for managing structured, semi-structured, and unstructured enterprise data.
• Define scalable data architecture patterns supporting analytics, AI/ML, reporting, and business intelligence.

Data Engineering & ETL Architecture

• Architect large-scale ETL/ELT frameworks supporting batch, streaming, and real-time data processing.
• Lead the architecture of scalable data pipelines integrating multiple enterprise source systems.
• Define data integration and processing patterns using Apache Spark, PySpark, Databricks, Cloudera/Hadoop, Kafka, and cloud-native services.
• Establish best practices for data ingestion, transformation, orchestration, processing, and delivery.
• Optimize data pipelines for performance, scalability, reliability, and cost efficiency.
• Provide architectural guidance to data engineering teams on modern data processing and integration patterns.

Cloud Architecture

• Design enterprise solutions across AWS, Microsoft Azure, and Google Cloud Platform (Google Cloud Platform).
• Lead cloud migration, modernization, and hybrid/multi-cloud architecture initiatives.
• Architect highly available, secure, scalable, and resilient cloud platforms.
• Define Infrastructure as Code (IaC), CI/CD, DevOps, and platform engineering strategies.
• Establish cloud governance standards covering security, networking, identity, monitoring, compliance, and cost management.
• Promote FinOps and cloud cost-optimization practices across enterprise platforms.

AI/ML & Advanced Analytics

• Define enterprise-wide AI/ML architecture and technology strategy.
• Architect MLOps frameworks supporting model development, deployment, monitoring, and lifecycle management.
• Design architectures for Generative AI, Agentic AI, RAG, LLMs, and enterprise AI applications.
• Define integration patterns for LLMs, vector databases, knowledge bases, and AI services.
• Establish AI governance, security, monitoring, responsible AI, and model-risk management practices.
• Enable predictive analytics, business intelligence, advanced analytics, and AI-driven business capabilities.

API & Enterprise Integration

• Define enterprise integration architecture and API strategy.
• Design API-first, microservices, event-driven, and distributed architectures.
• Establish integration patterns using REST APIs, GraphQL, Enterprise Service Bus (ESB), messaging systems, and streaming platforms.
• Ensure enterprise integrations are secure, scalable, reusable, resilient, and maintainable.
• Define standards for API governance, lifecycle management, security, monitoring, and observability.

Quality Engineering & Test Automation

• Define enterprise quality engineering and testing strategies across data, cloud, APIs, applications, and AI/ML platforms.
• Architect automated testing frameworks for ETL, data quality, APIs, cloud platforms, and AI/ML solutions.
• Drive adoption of CI/CD-integrated testing and quality engineering practices.
• Establish standards for reliability, performance, scalability, observability, and resilience testing.
• Promote automated validation and continuous quality across enterprise platforms.

Leadership & Stakeholder Management

• Serve as a trusted technology advisor to executive, business, and technology leadership.
• Partner with customers and business stakeholders to understand strategic objectives and translate them into technology solutions.
• Mentor architects, engineers, technical leads, and development teams.
• Lead architecture review boards, technical governance forums, and design discussions.
• Communicate complex technical concepts and architectural decisions effectively to both technical and executive audiences.
• Drive innovation and adoption of emerging technologies aligned with business strategy.

Required Technical Skills Data Platforms

• Databricks Lakehouse
• Cloudera
• Snowflake
• Hadoop Ecosystem - HDFS, Hive, Spark, YARN
• Data Lakes & Data Warehouses
• Delta Lake
• Data Mesh / Data Fabric

Data Engineering

• ETL / ELT Architecture
• Enterprise Data Pipelines
• Apache Spark / PySpark
• Kafka
• Batch & Real-Time Processing
• Streaming Architecture
• Data Quality & Observability

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (Google Cloud Platform)
• Cloud Security & Networking
• Infrastructure as Code - Terraform / CloudFormation
• Cloud Architecture & Migration

AI/ML

• Machine Learning Platforms
• MLOps
• Generative AI
• Large Language Models (LLMs)
• Agentic AI
• RAG Architectures
• Vector Databases
• AI Governance

API & Integration

• REST APIs
• GraphQL
• Enterprise Service Bus (ESB)
• Event-Driven Architecture
• Microservices
• Messaging & Streaming Frameworks
• API Management

DevOps & Automation

• CI/CD Pipelines
• GitHub / GitLab
• Jenkins
• Docker
• Kubernetes
• Infrastructure Automation
• Test Automation Frameworks

Database Technologies

• Snowflake
• SQL Server
• PostgreSQL
• Oracle
• NoSQL Databases
• Enterprise Data Warehousing

• Experience leading enterprise-wide data modernization and cloud transformation programs.
• Strong background in Databricks, Cloudera, Snowflake, and Hadoop-based ecosystems.
• Experience designing large-scale data platforms for analytics, AI/ML, and enterprise reporting.
• Experience working with distributed systems, high-volume data processing, and real-time streaming.
• Experience with enterprise architecture frameworks, governance models, and technology roadmaps.
• Consulting or customer-facing architecture experience is highly desirable.

Soft Skills

• Executive stakeholder management
• Strategic thinking and technology vision
• Enterprise architecture leadership
• Strong communication and presentation skills
• Technical consulting and customer engagement
• Problem-solving and decision-making
• Ability to influence across business and technology teams
• Strong leadership, collaboration, and mentoring skills