Abhinash Palagani

Data Engineer @JPMorganChase

US

Inherited fragmented, untested data models with no quality checks across 10 pipeline domains, architected 10 certified dbt Core medallion models (bronze → silver → gold) on Snowflake and Databricks with Delta Lake partitioning and Z-Ordering, cutting pipeline cycle time by 35% and improving data accuracy by 22%.• Faced a critical gap in early financial risk detection across 10M+ account data flows - built scalable ELT pipelines in PySpark and SQL ingesting structured and semi-structured data from Bulk API and REST API, SQL Server, and Kafka CDC-pattern streams, enabling identification of 300+ high-risk accounts and contributing $7M in annual risk outcomes.• Manual dbt model deployments across Dev, QA, and Production caused inconsistency and release delays -engineered full CI/CD automation using Git and Python scripting to promote data engineering artifacts across all environments, reducing deployment errors and improving data accuracy by 22%.• Data workflows ran with minimal visibility and frequent production failures across 10M+ account flows - orchestrated end-to-end pipeline monitoring using Apache Airflow-pattern orchestration and Stonebranch-equivalent workload automation, resolving production issues proactively and ensuring SLA-compliant pipeline performance.• Data science and analytics teams were spending 48+ hours per month manually preparing feature data for reporting and ML workflow, delivered fully automated, AI-ready dbt gold-layer pipelines with embedded quality checks and documentation, saving 48 analyst hours/month and providing ML teams with reliable, versioned feature inputs.

Sacred Heart University

Master of Science - MS, Data Science

Presidency University, Bangalore

Bachelor of Technology - BTech, petroleum engineering