RAVIKUMAR GANGISETTY

Toronto,ON

Summary

Data Platform Engineer with over four years of experience in designing and building scalable, cloud-native data platforms on AWS. Expertise in real-time and batch data processing using Apache Kafka, Spark, and Snowflake, effectively supporting high-volume distributed systems with over 10 million events per day. Proficient in DataOps, ETL/ELT pipelines, and Infrastructure as Code (Terraform), emphasizing performance optimization, data governance, and reliability with an impressive 99.9% uptime. Committed to enabling analytics and AI/ML workloads through robust data architecture while fostering collaboration within cross-functional teams and mentoring engineers to deliver enterprise-grade, data-driven solutions.

Overview

years of professional experience

Certification

Work History

Data Platform Engineer

Intuit, Canada

01.2024 - Current

Designed and implemented a scalable cloud-native data platform on AWS supporting real-time and batch processing of 10M+ events/day with sub-2-second latency
Built and optimized distributed data pipelines using Apache Kafka and Spark, improving data freshness by 85% and ensuring high scalability and fault tolerance
Developed and maintained ETL/ELT pipelines using Snowflake and dbt, improving query performance by 60% and enabling analytics-ready datasets
Architected data ingestion frameworks combining batch and streaming workloads, increasing data reliability by 40% and reducing data loss to
Applied DataOps principles and Infrastructure as Code (Terraform) to automate provisioning and deployment, reducing deployment time by 80%
Implemented data governance, validation, and monitoring frameworks, reducing inconsistencies by 65% and ensuring compliance and data quality
Designed data models (star/snowflake schema) enabling BI dashboards and reducing reporting time by 70%
Deployed observability solutions (Prometheus, Grafana), reducing Mean Time to Resolution (MTTR) by 50%
Built data pipelines supporting AI/ML workloads, enabling real-time predictive analytics and improving forecasting accuracy by 30%
Containerized and deployed applications using Docker and Kubernetes, improving scalability by 70% under variable workloads
Collaborated with cross-functional teams to deliver data-as-a-product solutions aligned with Data Mesh principles
Mentored junior engineers and contributed to data engineering standards and best practices

Software Developer

HCL Technologies, India

09.2021 - 12.2023

Designed and developed a cloud-based data platform on AWS, supporting high-volume processing with 99.9% availability
Built serverless data ingestion pipelines using AWS Lambda and Python, reducing operational costs by 20%
Engineered secure data storage solutions using Amazon S3 and IAM, ensuring compliance with data governance and security standards
Improved system throughput by 30% through performance optimization and monitoring strategies
Automated infrastructure provisioning and deployment using CI/CD pipelines and scripting, reducing manual effort by 40%
Developed scalable ETL workflows and data transformation pipelines, supporting analytics and reporting systems
Collaborated within agile teams to deliver high-performance, scalable data solutions

Education

Computer Science

GMR Institute of Technology

India

01-2021

Skills

Data Engineering & Architecture: Data Platform Engineering, Data Mesh (Data-as-a-Product), ETL/ELT Pipelines, Data Warehousing, Data Lakes, Distributed Systems

Programming & Querying: Python, SQL (Advanced Query Optimization), Shell Scripting

Big Data & Streaming: Apache Kafka, Apache Spark (Structured Streaming), Hadoop

Cloud & Infrastructure: AWS (S3, Lambda, IAM), Terraform (IaC), CI/CD Pipelines, Kubernetes, Docker

Data Platforms & Tools: Snowflake, dbt, Apache Airflow

Data Governance & Quality: Data Validation, Data Monitoring & Alerting, Data Security & Compliance

DevOps & DataOps: GitHub Actions, Infrastructure Automation, Observability (Prometheus, Grafana)

AI/ML Enablement: ML Pipelines, Predictive Analytics, Data Preparation for AI/ML Systems

Serverless computing

Collaboration and communication

Infrastructure as Code

Cloud infrastructure management

Linux system administration

DevOps methodologies

Certification

AWS Certified Data Analytics – Specialty (In Progress)
AWS Certified Solutions Architect – Associate (In Progress)
Databricks Certified Data Engineer Associate
Snowflake SnowPro Core Certification
NPTEL – Programming, Data Structures and Algorithms in Python
Coursera – Getting Started with Python
Coursera – Python Data Structures

Languages

English

Full Professional

Timeline