Description:

Byteridge is hiring a Rapid Prototyping Engineer (FDE) for its BI Solutions practice to engineer analytics delivery frameworks and AI-powered insights for enterprise customer accounts. Operating as a hybrid Solution Architect and Prototyping Developer across India's premier tech hubs, you will lead the end-to-end deployment of Amazon QuickSuite, QuickSight, and modern AWS data fabrics across complex multi-cloud environments. The responsibility centers on designing custom data pipelines, translating raw client specifications into automated data visualization models, and leveraging cutting-edge integrations like the Model Context Protocol (MCP) to configure AI agents, search spaces, and autonomous analytics workflows that directly bridge business metrics to real-time execution.

Key Technologies:

Python, Amazon QuickSight, AWS, Tableau, Power BI, Business Intelligence, Athena, Redshift

Requirements:

• Bachelor's degree in Computer Science, Data Science, Engineering, or equivalent practical technical experience.
• 4–6 years of professional hands-on engineering experience within business intelligence infrastructure, full-scale data analytics pipelines, or technical cloud consulting.
• Deep deployment proficiency across leading data visualization engines—with an explicit focus on Amazon QuickSight/QuickSuite, Tableau, and Microsoft Power BI.
• Practical capability constructing analytics layers inside the Amazon Web Services ecosystem—specifically query tuning via Amazon Athena, managing serverless ETL schedules through AWS Glue, and structuring data warehouses using Amazon Redshift.
• Strong programming depth in Python and JavaScript to engineer automated ETL pipelines, configure RESTful web services, and deploy reusable deployment accelerators.
• Foundational or hands-on familiarity working with modern AI-driven analytics architectures, including developing custom connectors via Model Context Protocol (MCP), setting up automated AI agents, constructing semantic search topics, and managing data knowledge bases.
• General engineering understanding of extracting, loading, and transforming structured or unstructured datasets rooted across heterogeneous multi-cloud environments (AWS, Azure, and GCP).
• Proven ability to partner directly with client engineering leads to capture abstract requirements, draft technical dashboards, map data governance boundaries, and lead self-service technical workshops.
• Superior problem-solving agility inside highly ambiguous technical scopes, strong multi-engagement task prioritizing habits, and data-driven communication skills to manage business stakeholders.