Mohan Teja Nandamudi

Build and maintain scalable cloud data platforms, ETL/ELT pipelines, CDC workflows, Lakehouse solutions, enterprise data models, governance frameworks, and event-driven data processing solutions.

• 01Improved pipeline reliability by 20% across enterprise reporting and analytics workloads.
• 02Reduced dashboard refresh time from 110 to 64 minutes and compute costs by 22%.
• 03Designed Lakehouse solutions using Amazon S3, Databricks Delta Lake, Medallion Architecture, DLT, Kafka, Kinesis, and Spark Structured Streaming.
• 04Built Star Schema and Snowflake Schema models with SCD Type 2 for BI, executive dashboards, and self-service analytics.

Modernized finance data warehouses and developed scalable batch and streaming pipelines across Google Cloud and Snowflake.

• 01Improved reporting performance by 40% and reduced infrastructure costs by 30% through warehouse modernization.
• 02Processed 1TB+ of enterprise data daily using Python, PySpark, Spark, Dataflow, Dataproc, Pub/Sub, Kafka, and GCS.
• 03Reduced query costs by 45% through BigQuery and Snowflake optimization.
• 04Reduced batch processing time by 35% and production incidents by 25%.

Modernized enterprise data warehouses and developed batch, streaming, and Lakehouse data solutions across Microsoft Azure and Snowflake.

• 01Improved reporting performance by 40% and reduced infrastructure costs by 30% through cloud data warehouse modernization.
• 02Reduced manual effort by 65% and improved pipeline reliability through scalable ETL/ELT and data integration pipelines.
• 03Reduced query costs by 36% through data warehouse, Lakehouse, partitioning, clustering, and SQL optimization.
• 04Reduced data latency by 22% through real-time and batch processing solutions.

Engineered ETL pipelines, batch and streaming data platforms, data warehouse models, BI dashboards, Airflow workflows, CDC, and data quality processes.

• 01Improved data availability by 17% through batch and streaming pipeline development.
• 02Reduced pipeline execution time by 23% through Spark and Hive optimization.
• 03Created Tableau and Power BI dashboards with row-level security, data validation, and KPI reporting.
• 04Improved pipeline reliability and SLA compliance through Airflow orchestration, CDC, metadata, lineage, and data quality checks.