AWS Data Platform Architect (AI & Graph)
- Compensation
- Not disclosed
- Location
- Bangalore, India
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Bachelor’s degree in Computer Science, Engineering, or related field
- 10+ years of experience in data architecture, data engineering, or cloud data platforms
- Proven experience designing and operating AWS-based enterprise data platforms
- Hands-on expertise with Redshift, Databricks, Athena, and Power BI in production environments
- Strong proficiency in SQL and Python, enabling development, optimization, and troubleshooting of data pipelines, queries, and cross-platform integrations
- Experience designing and implementing graph data models or graph databases
- Experience enabling or integrating AI/LLM use cases within data platforms
Nice to have
- Experience implementing CI/CD for data platforms
- AWS Professional-level certifications
- Familiarity with RAG architectures and LLM-driven data access patterns
Full description
Work Schedule
Standard (Mon-Fri)
Environmental Conditions
Office
Job DescriptionPosition Summary
We are seeking an experienced AWS Data Platform Architect to own and evolve our enterprise data platform. This role is responsible for designing a scalable, secure, and governed AWS-based data architecture that supports both current analytics and an AI-ready Data-as-a-Platform foundation.
You will ensure the platform is AI-ready by design, embedding capabilities such as RAG patterns, semantic access, and graph-based data modeling, while maintaining operational stability of existing analytics workloads across Redshift, Databricks, Athena, and Power BI.
This is a hands-on architecture leadership role that requires deep expertise in SQL, Python, and modern data platforms, with end-to-end ownership of data, analytics, and platform governance.
Key Responsibilities
Platform Architecture & Strategy
- Define and evolve the target AWS data platform architecture across ingestion, storage, transformation, semantic, and consumption layers
- Establish and enforce architecture standards for scalability, security, reliability, and cost efficiency
- Design integration and migration patterns across legacy and modern platforms (Redshift, Databricks, Athena, Power BI/Fabric, and semantic layers such as Cube/SPOT where applicable)
- Ensure platform evolution introduces new capabilities without disrupting existing reporting and analytics workloads
AI & Graph Enablement
- Design and implement AI/LLM-ready architecture patterns, including RAG-based retrieval and semantic data access
- Develop and integrate graph data models to support relationship-driven analytics and intelligence use cases
- Ensure the platform supports efficient data access patterns for AI workloads, across structured, semi-structured, and graph data
- Lead proofs-of-concept and reference implementations to validate AI and graph capabilities prior to production adoption
Platform Ownership & Optimization
- Provide architectural oversight for Redshift, Databricks, and Athena, ensuring performance optimization, workload governance, and cost efficiency
- Guide and review complex SQL transformations and workloads, ensuring scalability and performance across large datasets