Kirtan · Senior AWS / Databricks Data Engineer · 5+ yrs
Mid-LevelProof of scale
About Kirtan
Kirtan is a ML Engineer with 5+ years of experience in designing and implementing end-to-end data pipelines and cloud-native data solutions. He has a strong background in AWS and Databricks, optimizing environments for machine learning lifecycles.
5+ years of commercial experience in
Skills(15)
Why hire Kirtan?
Architected and implemented scalable real-time data pipelines using AWS IoT Core, Kinesis, and Databricks.
Led the strategic migration of 10 TB of data to Amazon Redshift, achieving significant performance improvements.
Integrated CI/CD pipelines for continuous deployment and monitoring across multiple data projects.
Achieved significant performance improvements and cost reductions post-migration of 10 TB of data.
Streamlined data ingestion and processing using parameterized scripts and automation from 50+ APIs.
Project highlights(6)
Real-Time Fuel Consumption Optimization Pipeline – Architect
Overview: Developed a scalable real-time data pipeline to optimize fuel consumption within a glass factory. Responsibilities: Architected and implemented a real-time data pipeline using AWS IoT Core and Kinesis for analytics and ML model training with Databricks. Managed data ingestion, transformation, and storage processes, ensuring optimal performance and cost efficiency. Integrated CI/CD pipelines for continuous deployment and monitoring.
Key outcomes:
Optimized fuel consumption within a glass factory using real-time sensor data.
Data Governance Framework Implementation – Lead
Overview: Implemented a comprehensive data governance framework to ensure data quality, security, and compliance across the organization. Responsibilities: Developed data policies, standards, and procedures, and established data stewardship roles. Defined data governance policies and standards. Implemented data quality checks and monitoring.
Key outcomes:
Ensured compliance and data quality across the organization through a comprehensive framework.
Build a Scalable Data Pipeline for Customer Churn Prediction – Designer and Implementer
Overview: Designed and implemented a scalable data pipeline using AWS Glue and Amazon S3. Responsibilities: Developed ETL processes using AWS Glue to extract data from multiple sources. Collaborated with data scientists to create a churn prediction model.
Key outcomes:
Enabled efficient data integration for machine learning models.
Modernizing Data Warehouse – Lead
Modernizing Data Warehouse — strategic 10 TB legacy on-prem → Amazon Redshift migration with assessment + ETL pipeline optimization + schema conversion. AWS Glue + AWS DMS + Databricks.
Key outcomes:
Achieved significant performance improvements and cost reductions post-migration.
Successfully migrated 10 TB of critical data assets and workloads to Amazon Redshift.
Ensured minimal downtime and data integrity throughout the migration process.
Creation of MVP Framework for Data Migration using AWS Glue and Amazon EMR – Developer
MVP Framework for Data Migration — leveraged AWS Glue + Amazon EMR + Amazon Redshift + Delta tables for diverse data sources + ML workflows.
Key outcomes:
Empowered enterprises to modernize their data infrastructure and optimize cloud-based analytics.
Delivered a scalable, efficient, and cost-effective data migration framework.
Industry experience
Manufacturing & Industrial
1 project- •Real-Time Fuel Consumption Optimization Pipeline— ArchitectAWS IoT Core · AWS Kinesis · Databricks · Python +1
Logistics & Supply Chain
1 project- •Modernizing Data Warehouse— LeadAmazon Redshift · AWS Glue · AWS DMS · Databricks
At a Glance
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Typically responds within 4 business hours.
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Kirtan
MLOps