lahari Peddakotla

I’m a Data Engineer with 5+ years of experience architecting and delivering end-to-end scalable data solutions across AWS, Azure, and GCP cloud ecosystems. I focus on high-throughput ETL/ELT, real-time streaming with Kafka and Spark Streaming, and enterprise data warehousing on Snowflake, BigQuery, and Azure Synapse. I build modern lakehouse architectures with PySpark, Python, SQL, Databricks, Apache Airflow, dbt, and Delta Lake.

In my most recent role, I architected a HIPAA-compliant Lakehouse on Microsoft Fabric, integrating Synapse Analytics, OneLake, and Dataflows Gen2 to collapse 50% of data silos and accelerate cross-domain availability by 200%. I designed secure ETL pipelines to ingest and transform 1M+ patient records into Delta Lake and Snowflake, and I improved real-time clinical delivery using Azure Stream Analytics and Kafka Streams by slashing end-to-end latency by 40%. I also delivered CI/CD automation with Azure DevOps and GitHub Actions, plus Terraform-based infrastructure-as-code, to reduce deployment time by 70% and strengthen governance aligned to HIPAA, GDPR, and CCPA.

I’ve also modernized enterprise pipelines in retail—moving legacy Informatica workflows to GCP-native architectures and scaling throughput by 3x—while rebuilding transformations as modular dbt models with partitioning, clustering, and SQL testing. Across domains, I prioritize measurable performance gains (e.g., 30–50% latency reductions), operational efficiency (e.g., 60% cuts in manual overhead), and reliability (95%+ SLA attainment). I’m at my best when I can combine engineering rigor, data governance (RBAC, IAM, audit logging, PII masking, lineage), and practical automation to help teams ship faster with confidence.