Saiteja Bachu

• Worked on Tableau-based data visualization solutions for operational and management reporting.
• Supported foundational ingestion frameworks and build metadata and transformation jobs across multiple data sources.

GenAI Data Engineering Use Case : (2025 – 2026)

• Built feature mart jobs for GenAI-based analytics workflows to reduce manual effort for analytics users.
• Integrated data from multiple sources to generate screening-ready datasets for automated narrative/report creation.
• Enabled analyst review workflows before submission to regulators such as MAS and RBI.
• Built, tested, deployed, and supported the end-to-end GenAI use case on a containerized CBD platform.
• Improved reporting turnaround time and reduced analyst toil through automation.

Build data marts for various Use cases: (2023 – 2025)

Worked on strategic enterprise data solutions supporting Fraud Detection, Board Risk Management Committee, Financial Crime Control Board, Transaction Surveillance Units, and Macro Payments Dashboard.

Key Contributions:

• Designed and developed scalable ETL/ELT pipelines to process high-volume payment and transaction data from multiple upstream systems.
• Built enterprise data marts for Macro Payments Dashboard and Board Risk Management Dashboard to support payment-level and transaction-level analytics.
• Enabled cross-border transaction monitoring frameworks for unusual flow of funds detection.
• Delivered data solutions supporting Anti Money Laundering (AML), sanctions compliance, and financial crime surveillance.
• Expanded customer screening capabilities from B2B to B2C customer base, significantly increasing compliance coverage.
• Optimized PySpark compute jobs and reduced BAU processing runtime from ~2 hours to ~15 minutes through partition tuning, code refactoring, and efficient transformations.
• Designed and implemented custom Apache Airflow DAGs for special-day/source-delay handling, reducing production failures and operational incidents.
• Built ingestion pipelines using multiple data sources:
• File-based ingestion
• API-based ingestion
• Database ingestion
• Real-time Kafka streaming pipelines
• Managed varying refresh frequencies including intraday, daily, and scheduled batch loads into AWS S3.
• Developed Tableau dashboards and analytical reporting solutions for business stakeholders.
• Supported production incidents, root cause analysis, SLA adherence, and operational stability improvements.