Mani Kanta Kalluru

I’m a Data Engineer with 3+ years of experience designing and optimizing scalable data pipelines and analytics solutions on Azure Databricks Lakehouse. I focus on building robust ETL/ELT workflows with reliable datasets that support reporting, forecasting, and business decision-making.

I’ve architected enterprise-grade Medallion (Bronze → Silver → Gold) Delta Lake frameworks using Databricks and PySpark to enable batch and near-real-time processing. My work helped reduce average data-to-dashboard latency by 45% and delivered datasets used by 4+ cross-functional business units.

I strengthen pipelines with data quality and observability practices like schema enforcement, data validation, reconciliation, anomaly detection, monitoring, alerting, audit logging, and RBAC. I’ve reduced pipeline defects by 35%, lowered data incident tickets by ~20 per month, and improved end-to-end runtime by 38% while reducing cluster compute spend by ~22%.

Across engineering teams, I’ve migrated legacy SQL Server workloads to cloud-native Azure Lakehouse, implemented CI/CD for Databricks notebooks and Azure Data Factory workflows with Azure DevOps, and prepared curated Gold-layer datasets supporting AI/ML use cases like demand forecasting and anomaly detection. I’m especially motivated by reliability, cost optimization, and delivering business impact through dependable data assets.