Presales Solution Architect - Data & AI

We are looking for a passionate, forward-thinking Presales Solution Architect – Data & AI (AWS) to help build intelligent, scalable, and data-driven solutions for our customers. In this role, you will architect next-generation analytics and Generative AI platforms on AWS, working closely with internal teams and clients to turn complex challenges into impactful cloud-native solutions.

The ideal candidate should have a deep understanding of data analytics technologies, Generative AI , AWS and AI/ML services and a track record of architecting scalable and efficient data and AI driven solutions to address complex business challenges.

Responsibilities :

• Presales Solution Architect – Data  & AI (AWS )to join our team will play a crucial role in designing and implementing data analytics and Generative AI solutions on the Amazon Web Services (AWS) platform. 
• Solution Design: Collaborate with multi-functional teams to capture requirements, design end-to-end data analytics solutions, and build architectural diagrams and documentation.
• AWS Expertise: Leverage your extensive knowledge of AWS services such as Amazon Redshift, Amazon EMR, Amazon Kinesis, AWS Glue, and others to architect robust data analytics platforms.
• Data Modeling: Design and optimize data models to support reporting, analytics, and business intelligence needs while ensuring data integrity and performance.
• Scalability and Performance: Architect solutions that are scalable, impactful, and cost-efficient, considering factors like data volume, query complexity, and user concurrency.
• Data Integration: Define data integration strategies to extract, transform, and load (ETL) data from various sources into the analytics platform, ensuring data consistency and quality.
• Security and Compliance: Implement security measures and standard processes to protect sensitive data and ensure compliance with industry standards and regulations.
• Solve and Optimization: Identify and resolve performance bottlenecks, data quality issues, and other challenges that may arise during implementation and operation.
• Collaboration: Work closely with data engineers, data scientists, business analysts, and collaborators to understand their needs and provide technical mentorship.
• Innovation: Stay up-to-date with industry trends and new technologies to recommend innovative solutions that improve our data analytics capabilities.