ARAVIND SRIRAM

• Collaborated with cross-functional teams to define requirements and develop end-to-end solutions for complex data engineering projects.
• Reengineered existing ETL workflows to improve performance by identifying bottlenecks and optimizing code accordingly.
• Ensured data quality through rigorous testing, validation, and monitoring of all data assets, minimizing inaccuracies and inconsistencies.
• Led development of data pipelines to aggregate and analyze security logs from multiple banking systems, resulting in a 30% increase in threat detection accuracy.
• Implemented automated data cleansing processes to ensure data integrity and compliance with regulatory standards.
• Collaborated with security analysts to design and deploy real-time monitoring solutions for detecting suspicious activities.
• Developed frame work to automate data enrichment by passing config file with relevant information.
• Enhanced log extraction and analysis by building real time log and parsing it in real time using AutoLoader framework and storing in delta lake which enhanced live diagnosing of attacks or malware

• PySpark based Dynamic Azure function to ingest API response to Data lake by flattening the response to CSV file
• Reduced Processing time by 50% and saved revenue by 30% by reducing API hits.
• Used Python, SQL, and Spark to collaborate by managing 2 junior data engineers to create a cloud-first data ingestion that improved processing speed of data along with cost by 30%
• Communicated with Data scientist to understand data needs, and translated their feedback into actionable data, saving 46 hours of manual work each month by automating the scheduling and monitoring using Airflow.

• Developed PySpark code to automate metrics monitoring of multiple dataset along with log metrics and query metrics basing on custom input
• Reduced monitoring time and saved 30% revenue and 50% of monitoring time.

• Designed and Drafted Databricks note books for generic ingestion from different structured source and Azure data factory for orchestration
• Created impact on development time for individual pipelines by 40% and saved cost by 30%.

• Developed Java/python frameworks to ingest and parse semi structured XML to JSON and XML to hive basing on custom mappings provided
• Created an impact in development time for upcoming pipelines which was used by multiple times.

• Improved crawler performance by parallelizing the crawling
• Reduced processing time from 10 days to 3hrs.

Automated Data profiling using PySpark both for incremental data on existing tables and on demand tables basing on request

• Improved performance 3X by migrating from java.

• Designed and Developed Data movement pipeline using python
• Decreased human power by 30hrs/week and saved $70000/year.