Atharva Akole

Data Engineer with 2.1+ Years Experience | Python, PySpark, AWS, SQL, ETL Pipelines, Big Data

Talent

PuneMember since 26 May 2026

PythonSQLPySparkKafkaS3GlueRedshiftCloudWatchETL PipelinesData TransformationData CleansingReal-Time Data ProcessingData ValidationData WarehousingApache AirflowAWS CloudWatchPerformance Optimization

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PythonSQLPySparkKafkaS3

About

Data Engineer with 2.1 years of experience in developing and managing ETL pipelines, cloud-based data solutions, and scalable data processing systems. Skilled in Python, SQL, PySpark, AWS, and Big Data technologies for handling large datasets and real-time data processing. Experienced in building data pipelines, data transformation workflows, and cloud data integration solutions.

Strong understanding of data warehousing, data modeling, and performance optimization techniques. Ability to work in Agile environments and collaborate with cross-functional teams to deliver reliable and scalable data engineering solutions.

Experience

Data Engineer - JDIT Business Solutions Pvt. Ltd. - Pune

(2024-03 - 2025-04)

Developed ETL pipelines using Python and PySpark for enterprise data processing.
Worked on AWS S3 and AWS Glue for cloud-based data integration and transformation.
Performed data cleansing and validation to improve data quality and accuracy.
Optimized SQL queries and Spark jobs for better system performance.
Created automated workflows for data ingestion from multiple data sources.
Worked with Kafka for real-time data streaming and processing.
Supported analytics and reporting teams by providing reliable datasets.
Monitored data pipelines using AWS CloudWatch and Splunk.
Participated in Agile sprint activities and production support tasks.

Data Engineer - SSGC - Pune

(2025-05 - 2026-03)

Designed and maintained scalable cloud-based data pipelines using AWS services.
Built PySpark transformation pipelines for processing large datasets.
Automated ETL workflows using Apache Airflow.
Developed real-time data processing pipelines using Kafka and Spark Streaming.
Worked on data warehousing and reporting solutions using Redshift.
Improved pipeline performance using query optimization and partitioning techniques.
Performed data validation and reconciliation to ensure data consistency.
Collaborated with business and analytics teams to understand data requirements.
Supported deployment, monitoring, and maintenance activities for production systems.