ناجاراجان Murugesan

| Fabric - (Client: United Group, USA | Domain:
• Built a scalable FHIR‑compliant data pipeline using Microsoft Fabric, Databricks, and Snowflake Iceberg, reducing file scanning time
by 40% and saving 50+ hours per week through early-stage regex-based filtering.
• Enabled seamless data ingestion and API integration by implementing elevated pipeline design within Microsoft Fabric, improving
overall processing efficiency and automation.
• Designed and orchestrated scalable pipelines using Azure Data Factory and Airflow (DAGs), leveraging Kafka for streaming data
pipelines and external service integration to process 30M+ records within an 8-hour scheduling window.
• Fine-tuned Data Lakehouse using Medallion Architecture (Bronze/Silver/Gold layers) using Delta Lake and advanced data
modelling, achieving 60% faster query performance, and reducing data load times by 30%.
• Implemented metadata-driven frameworks and data observability practices to enhance pipeline monitoring, reliability, and system
performance.
• Conducted root cause analysis, deployed robust batch processing solutions, incorporating monitoring and improve pipeline
reliability and ensure timely resolution of critical production issues within SLA.
• Enabled data pipelines and infrastructure deployment using CI/CD pipelines, reducing release cycles by 20%.
• Ensured high data quality through data validation, cleansing, and preprocessing techniques, reducing inconsistencies, and
improving overall data reliability.

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• Built and maintained scalable data pipelines using PySpark for data ingestion, transformation, and loading into Hive tables,
improving processing efficiency by 30%.
• Simplified reporting processes using SQL and SSRS, delivering daily, weekly, and monthly reports for order management, reducing
report generation time by 40%.
• Improved data accuracy by automating data extraction and validation processes, minimizing manual errors and achieving
consistent, high-quality reporting.
• Collaborated with cross-functional teams to integrate new data sources into existing pipelines, ensuring seamless data flow and
system alignment.
• Led and mentored a team of 8 members, delivering projects within tight deadlines and improving overall user satisfaction by 20%.
• Co-ordinated on premises to AWS cloud migration, enabling data integration through existing pipelines and managing large-scale
data storage in Amazon S3, improving system uptime by 30%.

• Tuned complex SQL queries and optimized PySpark transformations in Jupyter Notebooks by refining indexes, joins, partitions, and
execution plans, reducing execution time by 50%.
• Led migration of legacy databases to Oracle and data processing frameworks, improving overall performance by 25% and
supporting best practices in data design.
• Delivered stored procedures, triggers, views, PySpark ETL jobs, and automated reporting solutions, saving 15+ hours per week.
• Mentored a team of 5 developers through training on SQL optimization, PySpark development, and database management
techniques.

• Created and maintained 25+ reports for MediSave scheme enhancements (regulatory, operational, analytical) using SAS SQL and
PySpark-based data processing in Jupyter Notebooks, improving reporting efficiency by 30% through automation.
• Performed unit and system testing using SAS SQL, IBM DB2, and PySpark notebooks, resolving 50+ UAT queries and improving data
accuracy and stakeholder satisfaction.
• Developed standardized data extraction and transformation workflows using SAS SQL and PySpark in Jupyter Notebooks, reducing
manual effort by 30+ hours per month.
• Managed project documentation, including action plans and technical design specifications, supporting data quality initiatives and
PySpark-based reporting solutions.

Sale and Mortgage System (Client: Housing Development Board, Singapore | Domain: Ministry of National
Development)
• Gathered requirements and defined technical solutions for DB2/SAS-based reporting systems, achieving 95-98% unit testing pass
rates with less than 5% post-production defects.
• Delivered PL/SQL procedures and DB2 queries for mortgage reporting, improving performance by ~15% while identifying 85-90%
of defects during testing to ensure smooth UAT cycles.
• Rectified system defects and improved data quality, decreasing post-production issues by 70% and ensuring 99% reporting
reliability for mortgage systems.

ISA Reconciliation, Solvency II, and WL Bonus Rates (Client: Royal London Group, United Kingdom | Domain: Insurance)
• Prepared and optimized 40+ complex SQL programs, including 15+ new developments and enhancements, supporting regulatory
reporting for a UK-based insurance client.
• Enhanced DB2 query performance, achieving 40% reduction in execution time for batch jobs supporting financial reconciliation.

RCV - WMS Re-platform (Client: Macys, United States of America | Domain: Retail)
• Defined and executed development plans, including retrofit planning and implementation.
• Prepared CIT plans based on program specifications to ensure successful execution.