Ashok Sagar

About Ashok Sagar

Ever since I dove headfirst into the world of data, it\'s been a thrilling journey of discovery. I’ve always found joy in untangling complex datasets and transforming them into meaningful, actionable insights. Throughout my career, I\'ve had the opportunity to work with organizations of all sizes, applying my skills to a wide spectrum of real-world challenges. One of the most rewarding chapters in my career was at Clairvoyant for AIG, where I led the development of a centralized claims data platform. My team designed and built a robust lakehouse on AWS using Spark, Glue, Snowflake, and Databricks—streamlining data ingestion from dozens of legacy sources and enabling near real-time insights for claims, policy, and regulatory teams. Prior to that, during my tenure at IQVIA, I played a key role in migrating complex clinical trial datasets to Azure. We standardized data pipelines using Databricks and Delta Lake, helping researchers and statisticians unlock value from data that was once fragmented across global regions. That experience reinforced that data engineering is as much about collaboration and empathy as it is about technology. More recently, I’ve been working on building enterprise-scale data platforms in the banking domain, focusing on fraud detection and AI-driven insights. This includes designing feature engineering pipelines on Databricks and contributing to GenAI-based solutions such as automated case narrative generation, where structured data is transformed into meaningful summaries to assist investigators. Over the years, I’ve worked across AWS, Azure, and GCP, solving data challenges in industries like insurance, healthcare, and banking. I enjoy building secure, scalable, and production-grade data pipelines using tools like Spark, Databricks, Kafka, Airflow, and cloud-native services. Mentoring junior engineers and leading agile teams has been just as fulfilling as delivering production systems. Outside of work, I enjoy contributing to knowledge-sharing sessions, tech forums, and mentoring aspiring engineers taking their first steps into the data world. If you\'re looking for someone who can design resilient data systems, build scalable lakehouse architectures, integrate AI/GenAI capabilities, or simply discuss the finer details of Spark optimization and partitioning strategies—I’d love to connect. Skills: Cloud Data Engineering, Spark, Python, SQL, Scala, Databricks, AWS, Azure, GCP, Snowflake, Kafka, ETL/ELT, Data Lakehouse Architecture, GenAI, CI/CD, Data Modeling

Experience

1. Lead Data Engineer Gen Ai

   Barclays Bank US

   Jun 2025 — Present · NJ, US

   Designed and implemented a GenAI-powered AML investigation platform leveraging Databricks Lakehouse architecture to process high-volume transactional, customer, and behavioral datasets for financial crime detection.• Architected scalable data pipelines using Apache Spark (PySpark/SQL) within Databricks to ensure high-performance data ingestion, transformation, enrichment, and real-time analytical processing.• Built and managed Databricks Lakehouse layers (Bronze, Silver, Gold) to maintain structured data refinement, improve traceability, and support regulator-ready reporting.• Implemented Unity Catalog for centralized data governance, fine-grained access control, data lineage tracking, and secure multi-workspace data management across AML use cases.• Enforced robust data governance frameworks including role-based access control (RBAC), column-level security, audit logging, and encryption standards to safeguard sensitive U.S. financial data.• Developed GenAI-driven Suspicious Activity Report (SAR) narrative generation capabilities, integrating large language models (LLMs) in a controlled, explainable, and human-in-the-loop environment to ensure regulatory compliance and accuracy.• Designed explainability mechanisms and audit trails for AI outputs to meet expectations of U.S. financial regulators and internal compliance review teams.• Integrated model outputs with compliance dashboards and reporting systems to provide investigators with structured insights, risk scores, and summarized findings.• Collaborated with risk, compliance, and regulatory stakeholders to align technical implementations with U.S. AML laws, BSA requirements, and financial crime prevention standards.• Modernized legacy compliance infrastructure by migrating to cloud-native Databricks Lakehouse environments, improving scalability, resilience, and operational efficiency.

Education

• University of Mumbai

  Bachelor's degree, Computer Science

• Rome Business School

  Master of Science - MS, Data Science

• Rome Business School

  Master of Science - MS, Big Data

• University of Mumbai

  Bachelor of Science - BS

• University of Mumbai

  Bachelor of Science - BS, Computer Science

• University of Mumbai

  Bachelor of Science - BS, Computer Science

Skills

• Hive
• Oracle Database
• Apache Spark
• Sqoop
• Scala
• Hadoop