Ravishankar Gnanavelu

Data Engineer

Bengaluru, Karnataka, India1 yr 11 mos experience

Key Highlights

Designed serverless ETL frameworks for real-time data ingestion.
Improved pipeline efficiency by 35% through automation.
Reduced AWS operational costs by 25% with optimization.

Stackforce AI infers this person is a Data Engineer specializing in AWS-based data solutions for enterprise applications.

Contact

ravishankargnanavelu77@gmail.com LinkedIn

Skills

Core Skills

AwsData Engineering

Other Skills

AWS GlueAWS LambdaAWS Step FunctionsS3DynamoDBRedshiftPySparkPythonData QualitySQLData LakesData PipelinesPandasShell ScriptingData Build Tool (DBT)

About

I’m a Data Engineer with 2 years of experience building scalable, event-driven, and AI-integrated data pipelines. My work bridges data engineering and machine learning, focusing on automation, optimization, and cloud-native orchestration using AWS services. At LLMNextGen2AI Technologies, I’ve designed and deployed serverless ETL frameworks that integrate AWS Glue, Lambda, and Step Functions to enable real-time and batch data ingestion. I’ve also engineered AI-ready pipelines supporting LLM fine-tuning and inference workflows — improving pipeline efficiency by 35% and reducing AWS operational costs by 25% through optimization and automation. My core strength lies in end-to-end data architecture, from ingestion and transformation to governance and analytics enablement, ensuring reliable and cost-efficient data ecosystems. I’m particularly passionate about metadata-driven orchestration, Medallion Architecture (Bronze–Silver–Gold), and cloud-first design patterns that empower AI and analytics platforms. 🔹 Core Skills: Python | SQL | PySpark | AWS (Glue, Lambda, Step Functions, S3, Redshift, DynamoDB) | Snowflake | ETL Design | Airflow | Data Warehousing | Cloud Automation 🔹 Interests: Event-driven architecture, AI-integrated data systems, and modern data stack orchestration

Experience

1 yr 11 mos

Total Experience

1 yr 11 mos

Average Tenure

1 yr 11 mos

Current Experience

Llmnextgen2ai private limited

AWS Data Engineer

Jun 2024 – Present · 1 yr 11 mos · Bengaluru · On-site

AI-focused data engineering company building serverless data platforms and LLM-integrated pipelines for enterprise clients in India.
Designed and deployed serverless ETL pipelines using AWS Glue, Lambda, and Step Functions to ingest data from 4+ source systems (APIs, databases, flat files) into a centralized data lake on S3, supporting both real-time and batch processing.
Built data pipelines for AI/ML workflows, preparing and transforming training datasets for LLM fine-tuning and inference — enabling the data science team to iterate on models 35% faster by eliminating manual data prep steps.
Implemented a data quality framework with custom validation rules across ingestion, transformation, and loading stages — catching schema drift and anomalies before they reached production analytics dashboards.
Optimized PySpark jobs by implementing dynamic partitioning, broadcast joins, and partition pruning — reduced average batch job runtime from ~45 minutes to ~29 minutes.
Automated data lifecycle management across S3, DynamoDB, and Redshift using lifecycle policies and intelligent job scheduling — reduced monthly AWS spend by approximately 25%.
Built monitoring and alerting for pipeline failures using CloudWatch and SNS, reducing mean time to detection from hours to under 5 minutes.

AWS GlueAWS LambdaAWS Step FunctionsS3DynamoDBRedshift+4

Learnmoretechnologies

Data Engineer

Jan 2024 – May 2024 · 4 mos · Bengaluru · On-site

Data engineering services firm specializing in AWS-based ETL solutions and analytics pipeline development.
Built and automated ETL pipelines using AWS Glue and PySpark to process 250K+ records per batch from client databases, transforming raw data into analytics-ready datasets in Redshift.
Optimized slow-running SQL queries used by the analytics team — improved query performance by 30% through indexing, query restructuring, and materialized views.
Automated previously manual data ingestion workflows with Lambda triggers and S3 event notifications, reducing manual intervention by 40% and freeing the team for higher-value work.
Developed custom Python validation scripts to enforce data quality standards — maintained 99.8% data accuracy across all pipelines during the engagement.

AWS GluePySparkSQLPythonAWSData Engineering