Roles & responsibilitiesHere are some of the key responsibilities of AI Cloud Architect :
- Architecture Design: Lead the design and implementation of data architecture solutions, utilizing hyper-scalers like GCP, AWS or Azure to enable data integration, management, and analytics across various data sources.
- Data Strategy Development: Develop and execute a comprehensive data strategy that aligns with business goals, ensuring best practices in data governance, quality, and security.
- Create architectural pattern and crucial components of a software architecture. Create reusable solution to a commonly occurring problem in software architecture in a certain context. Apply Design pattern best practice to solve common problems when designing an application or system.
- Cloud Solutions: Architect and implement cloud-based data solutions, leveraging services such as AI Foundry/Vertex AI/AWS Sagemaker, AKS/EKS/GKE, BigQuery, Cloud Storage/CosmosDB/S3, Azure DevOps/AWS CodePipeline, etc.
- Performance Optimization: Monitor and optimize the performance of data solutions, ensuring high availability, scalability, and reliability.
- Mentorship: Provide technical leadership and mentorship to junior data architects and engineers, fostering a culture of continuous learning and improvement.
- Strong knowledge of Data Management principles. Experience in building ETL / data warehouse transformation processes. Designing and implementing highly performant data ingestion pipelines from multiple sources using Apache Spark and/or Azure Fabric/Databricks. Hands on experience in Databricks/Microsoft Fabric, Data factory, Python, Scala/PySpark, Data Lake (Delta Lake/One Lake).
- Develop a full stack cloud architecture for life cycle management. Define components of the architecture that can be sourced externally (open source) or built-in house. Work with a diverse team of architects and engineers both within and across product teams to collaboratively build a cohesive architecture.
- Strong knowledge of Databricks or Microsoft Fabric and all its components. Design and implement secure data architectures using Databricks or Microsoft Fabric, including secure connectivity between it and other resources. Evaluate projects suitability for utilizing Databricks/MS Fabric vs alternative solutions.
- Stay informed about the Databricks or Microsoft Fabric release roadmap, participate in private previews, and evaluate the impact of future functionality on our solutions.
Mandatory technical & functional skills
- Microsoft Azure/GCP/AWS (Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as Service (SaaS)
- Microsoft Fabric, including designing and implementing Pipelines, PySpark Notebooks, Lakehouses, Warehouses, Dataflows, and Semantic Models.
- AI platforms like Azure Foundry/AWS SageMaker/Vertex AI.
- Rapid Development / Rapid Prototyping development experience.
- Experience with Big Data solutions on Azure/GCP/AWS and delivering data pipelines (e.g. Azure Data Lake Service/AWS S3/Cloud Storage, Power BI, Azure Synapse/Redshift/BigQuery and Azure Data Factory/AWS Glue/Cloud Data Fusion)
- Managing Networking, Virtualization, Blue-Green Deployments, Identity, security, business continuity, disaster recovery, data platforms, and governance.
- Cloud Architecture Certifications with Azure/GCP/AWS
Preferred Technical & Functional Skills
- Understanding of GenAI technologies
- Good understanding on Cloud Security
Key behavioral attributes/requirements
- Ability to mentor junior developers
- Ability to own project deliverables, not just individual tasks
- Understand business objectives and functions to support data needs
RESPONSIBILITIES
Roles & responsibilitiesHere are some of the key responsibilities of AI Cloud Architect :
- Architecture Design: Lead the design and implementation of data architecture solutions, utilizing hyper-scalers like GCP, AWS or Azure to enable data integration, management, and analytics across various data sources.
- Data Strategy Development: Develop and execute a comprehensive data strategy that aligns with business goals, ensuring best practices in data governance, quality, and security.
- Create architectural pattern and crucial components of a software architecture. Create reusable solution to a commonly occurring problem in software architecture in a certain context. Apply Design pattern best practice to solve common problems when designing an application or system.
- Cloud Solutions: Architect and implement cloud-based data solutions, leveraging services such as AI Foundry/Vertex AI/AWS Sagemaker, AKS/EKS/GKE, BigQuery, Cloud Storage/CosmosDB/S3, Azure DevOps/AWS CodePipeline, etc.
- Performance Optimization: Monitor and optimize the performance of data solutions, ensuring high availability, scalability, and reliability.
- Mentorship: Provide technical leadership and mentorship to junior data architects and engineers, fostering a culture of continuous learning and improvement.
- Strong knowledge of Data Management principles. Experience in building ETL / data warehouse transformation processes. Designing and implementing highly performant data ingestion pipelines from multiple sources using Apache Spark and/or Azure Fabric/Databricks. Hands on experience in Databricks/Microsoft Fabric, Data factory, Python, Scala/PySpark, Data Lake (Delta Lake/One Lake).
- Develop a full stack cloud architecture for life cycle management. Define components of the architecture that can be sourced externally (open source) or built-in house. Work with a diverse team of architects and engineers both within and across product teams to collaboratively build a cohesive architecture.
- Strong knowledge of Databricks or Microsoft Fabric and all its components. Design and implement secure data architectures using Databricks or Microsoft Fabric, including secure connectivity between it and other resources. Evaluate projects suitability for utilizing Databricks/MS Fabric vs alternative solutions.
- Stay informed about the Databricks or Microsoft Fabric release roadmap, participate in private previews, and evaluate the impact of future functionality on our solutions.
Mandatory technical & functional skills
- Microsoft Azure/GCP/AWS (Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as Service (SaaS)
- Microsoft Fabric, including designing and implementing Pipelines, PySpark Notebooks, Lakehouses, Warehouses, Dataflows, and Semantic Models.
- AI platforms like Azure Foundry/AWS SageMaker/Vertex AI.
- Rapid Development / Rapid Prototyping development experience.
- Experience with Big Data solutions on Azure/GCP/AWS and delivering data pipelines (e.g. Azure Data Lake Service/AWS S3/Cloud Storage, Power BI, Azure Synapse/Redshift/BigQuery and Azure Data Factory/AWS Glue/Cloud Data Fusion)
- Managing Networking, Virtualization, Blue-Green Deployments, Identity, security, business continuity, disaster recovery, data platforms, and governance.
- Cloud Architecture Certifications with Azure/GCP/AWS
Preferred Technical & Functional Skills
- Understanding of GenAI technologies
- Good understanding on Cloud Security
Key behavioral attributes/requirements
- Ability to mentor junior developers
- Ability to own project deliverables, not just individual tasks
- Understand business objectives and functions to support data needs
QUALIFICATIONS
This role is for you if you have the below
Educational Qualifications
- Bachelor's degree in Computer Science
- Experience of designing and building applications using microservices & Hands on application of design patterns for building enterprise applications
Work Experience
- 12 + years of experience in Cloud Data Architecture.
#KGS
