Eshwar Kumar Akurathi
Business Analyst @Amazon | Gen Ai | Amazon Bedrock |DataZone | Sagemaker| Lake Formation | Lambda | Athena | SNS | Python | Pyspark | SQL | Glue | Redshift | EMR | ETL & Data pipelines | AWS - SAA certified .
- Role
- Business Analyst at Amazon
- Location
- Bengaluru, KA, IN
- LinkedIn followers
- 500 followers
About Eshwar Kumar Akurathi
As a Aws Data Engineer with 2+ years of experience, I specialize in building scalable, cloud-native data solutions using AWS and Databricks. I have hands-on expertise in designing ETL pipelines, implementing data lakes with Delta Lake, and optimizing large-scale data workflows with PySpark and SQL.My experience includes:Developing data pipelines using AWS Glue, Lambda, and Redshift.Integrating structured and unstructured data from diverse sources into unified storage systems on S3.Automating workflows with Apache Airflow and AWS Step Functions to ensure smooth data operations.Working with cross-functional teams to support machine learning initiatives by transforming and preparing high-quality datasets.Enhancing system performance through partitioning, caching, and query optimization in Redshift and Databricks.I am passionate about leveraging cloud technologies to drive data-driven decision-making and continuously seek opportunities to grow and innovate in the evolving world of data engineering.
Experience
Business Analyst
Sep 2025 — Present · Bengaluru, IN
AI-Powered Natural Language Data Query System using Amazon Bedrock & AthenaDesigned an automated data ingestion pipeline where dataset files uploaded to Amazon S3 trigger AWS Glue Crawlers to create and update schema in the Glue Data Catalog.Implemented synchronization of dataset metadata stored in a separate S3 bucket into the Amazon Bedrock Agent Knowledge Base for contextual understanding.Built an intelligent query interface using Amazon Bedrock Agents to convert natural language questions into optimized SQL queries.Developed AWS Lambda functions to dynamically execute generated SQL queries on Amazon Athena and fetch query results.Implemented response transformation logic to convert raw SQL output into human-readable insights before returning to users via the agent.Enabled non-technical users to perform complex data analysis without writing SQL, improving accessibility and decision-making speed.Designed the system using fully serverless components for high scalability, fault tolerance, and low operational cost.Tech Stack:Amazon S3, AWS Glue Crawler, Glue Data Catalog, Amazon Athena, Amazon Bedrock Agents, Knowledge Bases, AWS Lambda, Python, IAM
Education
KL University
Bachelor of Technology , CSE
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