Muktha Nanda Reddy Kamidi
Professional Summary
Data Engineer with 4 years of experience building scalable ingestion, transformation and streaming pipelines for healthcare, insurance and retail analytics. Strong hands-on experience with Databricks and PySpark for large-scale ETL, Azure Data Factory and Event Hub for Azure-based ingestion, and AWS Glue, S3 and Step Functions for cloud ETL orchestration. Skilled in optimizing query performance in Azure Synapse and Amazon Redshift, implementing CDC and incremental loads, and enforcing data quality via Python validation frameworks and anomaly detection. Experienced in designing dimensional models and star schemas for BI, automating database schema changes with Liquibase and Git, and instrumenting pipelines with Azure Monitor and CloudWatch for reliable operations. Proven ownership of end-to-end pipelines, security controls, and CI/CD for production data platforms.
Technical Skills
Work Experience
- Designed scalable ingestion pipelines using Azure Data Factory and Azure Event Hub to centralize healthcare data for analytics and downstream BI.
- Developed reusable transformation workflows in Databricks with PySpark to standardize schemas and accelerate ETL development for clinical datasets.
- Optimized complex SQL and materialized views in Azure Synapse to improve report response times by 24%.
- Implemented Python-based automated validation checks and anomaly detection to ensure record-level data integrity before load.
- Built incremental CDC pipelines combining Databricks and Azure Data Factory to minimize redundant transfers and shorten ingest windows.
- Automated CI/CD deployments for database and pipeline changes using Git and Liquibase to standardize releases and reduce deployment errors.
- Instrumented Azure Monitor logs and implemented retry logic for pipeline resilience, improving operational visibility and fault recovery.
- Built AWS Glue ETL pipelines and S3-based ingestion workflows to centralize insurance datasets, improving ingestion efficiency by 26%.
- Designed large-scale PySpark transformation logic to scale processing and increase reliability for downstream analytics consumers.
- Improved Redshift query performance through schema tuning and distribution key optimization to accelerate business reporting.
- Implemented Python data quality validation scripts to enforce schema and value checks prior to load into Redshift.
- Streamlined orchestration with AWS Step Functions and instrumented pipelines using AWS CloudWatch for operational visibility and simpler failure handling.
- Integrated Kafka producers with Spark consumers to enable near-real-time event processing and reduce latency for event-driven analytics.
- Analyzed retail operations data using SQL to extract trends and KPIs that informed stakeholder decisions and reporting priorities.
- Built interactive Power BI dashboards and reusable templates to standardize reporting across retail teams and increase visibility.
- Created Python-based data cleaning and validation workflows to improve dataset quality for analytics consumption.
- Automated SQL-based reporting pipelines, reducing manual report preparation by 23%.
- Integrated multiple data sources through scripting and automation to improve accessibility and timeliness of analytics datasets.
- Optimized SQL queries and ETL processing logic to reduce execution time and support faster analytical cycles.
Education
Certifications
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