Abhinash Palagani
Data Engineer @JPMorganChase
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WORK HISTORY
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.
EDUCATION
Sacred Heart University
Master of Science - MS, Data Science
Presidency University, Bangalore
Bachelor of Technology - BTech, petroleum engineering
ABOUT ABHINASH PALAGANI
The next wave of enterprise AI runs on clean, scalable, real-time data infrastructure. I built that infrastructure.As a Data Engineer with 5+ years at JP Morgan Chase and S&P Global, I sit at the intersection of two of the hottest hiring priorities in 2026, cloud data engineering and AI-ready data platforms. My work powers not just dashboards and reports, but the ML pipelines, feature stores, and governed data layers that production AI systems depend on.At JP Morgan Chase, I architected 10 certified dbt Core data models on Snowflake and Databricks using medallion architecture, implemented Delta Lake partitioning and Z-Ordering that cut pipeline cycle time by 35%, and delivered data quality systems that contributed to $7M in annual outcomes, including early detection of 300+ high-risk accounts. I also built and deployed CI/CD automation for dbt model promotion across Dev, QA, and Production, saving 48 analyst hours per month and giving ML teams reliable, versioned feature inputs they can trust.At S&P Global, I designed cloud-native lakehouse architectures across 5M+ accounts with 97% data precision, reduced data freshness latency by 23% through Kafka-pattern CDC streaming, and enforced cross-domain data governance standards that directly support AI model lineage and compliance, a growing requirement in regulated financial environments.What makes me different from a conventional data engineer: I design pipelines with the ML consumer in mind. That means building feature-ready gold layers in dbt, orchestrating retraining-compatible Airflow DAGs, and structuring Delta Lake schemas so data scientists can iterate without re-engineering. In a world where AI moves to production only as fast as its data infrastructure allows, that mindset is the difference between a pipeline and a competitive advantage.Core stack:→ AWS (S3, Glue, Lambda, Redshift, MWAA) · Databricks · Snowflake, CRM Salesforce→ dbt Core & Cloud · Delta Lake · Medallion Architecture→ PySpark · Apache Airflow · Kafka-pattern Streaming→ ML Pipeline Support · Feature Store Design · AI Data Governance→ CI/CD · Git · DataOps · MLflow (familiar) · Python · SQLMS in Computer and Information Science - Sacred Heart University.Open to: Senior Data Engineer · AI/ML Data Engineer · Data Platform Engineer · MLOps-adjacent roles.Let\'s connect if you\'re building or scaling AI-grade data infrastructure in fintech or enterprise tech. a••••••••@gmail.com | Jersey City, NJ - Open to NYC metro & Allover USA
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