Ayush Verma

• ETL development: Did Analysis data pipelines for fraud-detection data models using Pyspark ,Hive while optimizing the codes. Advanced SQL techniques, including complex joins and window functions, were used to uncover actionable insights, ensuring data accuracy and completeness for downstream applications.
  ◦ Transactional monitoring: Used AML scenarios like Rapid Movements ,External Transfer , High Risk Counter Parties to
  detect fraud and Generate alert which can be used furthur for Analysis
  ◦ Data Processing Optimization: Enhanced the efficiency of data pipelines by adding scenarios in the existing workflows with PySpark. Achieved significant performance gains by implementing parallel processing and tuning Spark configurations, reducing ETL processing times and enabling faster access to data for analytics .
  ◦ Data Warehousing: Built and maintained scalable data warehousing solutions using Apache Hive. Implemented
  advanced analytics queries and integrated data from disparate sources to enable complete reporting and analysis,
  ensuring the warehouse met the scalability and reliability needs of cloud-based applications.
  ◦ Leadership: Provided mentorship to junior team members by conducting technical training sessions and code reviews.
  Encouraged best practices in coding, data modeling, and cloud deployment, fostering a culture of continuous learning and
  collaboration within the team.

◦ Extraction, Transfer and Load (ETL) Development and Optimization: Designed and implemented robust ETL
pipelines related to transactional monitoring for PNC Bank. Utilized PySpark to handle large-scale data extraction,
transformation, and loading, ensuring data accuracy and integrity across all workflow stages.
◦ Data Extraction and Ingestion: Automated data extraction and ingestion processes from diverse sources, including
relational databases, APIs, and HDFS storage systems. Leveraged PySpark and Hive to streamline
data integration, reduce latency, and detect money laundering and identify suspicious activity leveraging information.
◦ Detection of fraudulent transaction: Data Engineer with 5+ years of experience designing and implementing data pipelines, ETL workflows, and analytics solutions for financial crime detection, including money laundering. Skilled in leveraging big data technologies and data query learning models to ensure compliance with Anti-Money laundering (AML) regulations in various categories like cash/card,checks,Electronic-fund transfer for a financial client across Canada and the
United States.
◦ Insight Deck Delivery: Developed comprehensive quarterly insight decks by processing and analyzing large datasets
using PySpark. Collaborated with business stakeholders to identify key metrics and deliver actionable insights, enabling datadriven
decision-making and aligning with organizational goals.
◦ Performance Tuning: Optimized data processing performance by implementing best practices in PySpark and ETL
tools. Tuned configurations to handle large data volumes efficiently, reducing processing times and improving overall
system performance.
◦ Collaboration and Reporting: Worked closely with cross-functional teams, including data analysts and business
leaders, to understand requirements and deliver insights aligned with business objectives