About the Role

CloudAI Technologies is seeking an experienced Cloud Data Architect to lead the architecture and design of modern enterprise data platforms across AWS and Azure, with strong expertise in Databricks, Snowflake, data lakes, and lakehouse architectures.

This is a senior technical leadership role responsible for defining data architecture, technology standards, reference architectures, and implementation patterns for complex cloud data modernization initiatives.

The Cloud Data Architect will work closely with customers, solution architects, data engineers, AI engineers, application teams, and CloudAI leadership to translate business and data requirements into scalable, secure, governed, and cost-effective cloud data platforms.

The ideal candidate combines robust architectural capabilities with sufficient hands-on engineering experience to guide implementation teams, review designs and code, troubleshoot complex problems, and ensure architectural decisions translate successfully into production systems.

Key Responsibilities

- Define end-to-end architecture for enterprise data lakes, lakehouses, cloud data warehouses, and modern data platforms.
- Architect solutions using Databricks, Snowflake, AWS, and Azure.
- Develop reference architectures, technical standards, design patterns, and engineering guidelines for CloudAI's data practice.
- Design scalable architectures for data ingestion, transformation, storage, processing, governance, analytics, AI/ML, and data consumption.
- Define appropriate technology choices based on workload, performance, scalability, security, cost, and business requirements.
- Design batch, streaming, event-driven, CDC, and near-real-time data architectures.
- Architect medallion and layered lakehouse architectures, including raw/bronze, refined/silver, and curated/gold data layers.
- Design enterprise data models and patterns supporting operational reporting, analytics, BI, AI/ML, Generative AI, and downstream applications.
- Establish standards for data quality, metadata management, lineage, cataloging, governance, security, privacy, and access control.
- Define patterns for master/reference data, common data models, semantic layers, and governed enterprise data products.
- Architect integration patterns across databases, SaaS applications, APIs, files, cloud storage, streaming platforms, and enterprise applications.
- Establish engineering patterns for Python, SQL, Spark/PySpark, Delta Lake, Databricks, and Snowflake.
- Define CI/CD, Infrastructure-as-Code, automated testing, observability, monitoring, and deployment standards for data platforms.
- Establish architecture for high availability, disaster recovery, scalability, resiliency,



and production operations.
- Lead performance and cost optimization strategies across cloud compute, storage, Databricks, Snowflake, and data-processing workloads.
- Conduct architecture reviews, design reviews, and technical assessments.
- Provide technical leadership and mentorship to data engineers and senior engineers.
- Work directly with customer technical and business stakeholders during discovery, architecture, and implementation.
- Translate complex business requirements into target architectures, implementation roadmaps, and technical work packages.
- Support cloud and data modernization assessments and migration planning.
- Participate in technical solutioning, proposals, estimates, presentations, and customer workshops.
- Collaborate closely with CloudAI's AI practice to design data platforms that support Generative AI, RAG, enterprise search, semantic retrieval, machine learning, and agentic applications.
- Evaluate emerging data technologies and recommend their adoption where they provide meaningful business or technical value.

Required Qualifications

- 10+ years of professional experience in data engineering, data architecture, data platforms, or related disciplines.
- Demonstrated experience architecting and delivering enterprise-scale cloud data platforms.
- Robust expertise with Databricks and/or Snowflake.
- Strong architecture and implementation experience with AWS and/or Microsoft Azure.
- Demonstrated experience designing data lakes and lakehouse architectures.
- Strong understanding of modern cloud data architecture and distributed data-processing patterns.
- Advanced understanding of SQL, Python, Spark/PySpark, and data engineering principles.
- Strong knowledge of data warehousing, dimensional modeling, data modeling, and analytical architectures.
- Experience designing ETL/ELT, batch, streaming, CDC, and API-based integration architectures.
- Strong understanding of cloud storage technologies such as Amazon S3 and Azure Data Lake Storage.
- Experience with metadata management, data lineage, data catalogs, governance, data quality, and security.
- Experience architecting CI/CD and automated deployment approaches for cloud data platforms.
- Strong understanding of cloud security, identity/access management, encryption, networking, and enterprise security requirements.




- Experience with performance optimization, capacity planning, and cloud cost optimization.
- Experience leading architecture and design discussions with engineering teams and customer stakeholders.
- Ability to communicate complex technical concepts effectively to both technical and non-technical audiences.
- Demonstrated ability to provide technical leadership across multiple concurrent projects or engineering teams.

Preferred Qualifications Experience with several of the following is highly desirable:

- Databricks Unity Catalog and modern Databricks platform capabilities
- Delta Lake
- Snowpark and advanced Snowflake architecture
- AWS Glue, EMR, Redshift, Athena and related AWS data services
- Azure Data Factory, ADLS, Synapse and Microsoft Fabric
- Kafka, Amazon Kinesis, or Azure Event Hubs
- dbt
- Terraform or other Infrastructure-as-Code technologies
- Docker and Kubernetes
- OpenMetadata or similar data catalog/governance platforms
- REST APIs and enterprise integration architectures
- DataOps and DevOps practices
- Enterprise semantic layers and common data models
- Vector databases and hybrid/vector search
- MLOps and AI/ML data architectures

AI & Modern Data Platform Experience Because CloudAI's data platforms increasingly serve as the foundation for AI solutions, the ideal candidate should understand how enterprise data architecture supports:

- Generative AI and Large Language Models
- Retrieval-Augmented Generation (RAG)
- Structured and unstructured enterprise search
- Vector and semantic search
- Natural-language querying of enterprise data
- AI/ML feature and training pipelines
- Enterprise knowledge platforms
- AI agents and agentic workflows
- Data governance and security for enterprise AI

The Cloud Data Architect does not need to function as the primary AI engineer but should be capable of designing the data architecture required to support production enterprise AI solutions.

Leadership Expectations

This is not a purely conceptual architecture position. The Cloud Data Architect is expected to remain technically engaged throughout delivery.

The successful candidate should be comfortable moving between customer architecture discussions, whiteboarding target-state platforms, reviewing implementation designs, guiding engineers, evaluating technologies, troubleshooting complex technical issues, and validating that delivered solutions conform to the intended architecture.

The role will also contribute to developing CloudAI Technologies' broader Data & AI practice, including reusable architectures, accelerators, engineering standards, technical capabilities, and solution offerings.

📌 Cloud Data Architect (Hyderabad)
🏢 CloudAI Technologies
📍 Hyderabad