Engineering Scalable Data. Delivering Actionable Insights.

I provide end-to-end Data Engineering and Data Analytics solutions, helping businesses transform raw data into reliable, scalable, and actionable datasets.

I specialize in PySpark, Apache Spark, Python, SQL, AWS, Snowflake, and Big Data technologies, with experience building and supporting large-scale enterprise data platforms.

I can help you design and develop ETL/ELT pipelines, batch and event-driven processing, data ingestion, transformation, migration, incremental processing, data quality, reconciliation, and analytics solutions.

On AWS, I work with Glue, EMR, S3, Lambda, Step Functions, SQS, EventBridge, Athena, and AWS CDK to build scalable, serverless, and event-driven data pipelines.

For Data Analysis & Analytics, I can do SQL-based analysis, data profiling, trend analysis, aggregations, KPI calculations, data validation, reconciliation, anomaly identification, and transforming complex datasets into meaningful business insights.

I also specialize in PySpark/Spark performance optimization, including complex joins, data skew, partitioning, large-scale transformations, incremental processing, and troubleshooting slow or failing pipelines.

Whether you need a data pipeline built from scratch, existing pipeline optimization, data migration, data quality solution, or analysis of your datasets, I can provide a scalable, maintainable, and production-focused solution.