SATISH KARUTURI

Project: Trade Fraud Risk Management

Description: Trade Fraud Risk Management (TFRM) aims at reducing the Trade frauds by creating client specific Fraud & Anti-Money Laundering (AML) scenarios by leveraging big data from multiple sources.

Technologies: Quantexa, Apache Spark, Scala, Python, HDFS, Hive, Kibana and Control-M

Roles and Responsibilities:

• Developed ETL processes using Apache Spark for efficient data transformation including extracting and parsing the data from the different internal and external sources.
• Developed scenarios to find out the risk customers and invoices over the fraud analysis.
• Collaborated with cross-functional teams to optimize data architecture and storage solutions.
• Played an integral role in the design and development of ingesting MANTAS India data from Standard Chartered's private Azure data lake into on-prem.
• Played a key role in the migration of ECDP -> Client Central, Hogan -> EBBS deliveries. Leveraged Quantexa’s ETL and scoring pipeline for solutioning. Liaised with cross functional teams in requirements analysis and documentation.
• Played a critical role in the design and development of adding multiple new interfaces to the existing Fraud Detection framework built with Quantexa to improve the bank’s ability to identify trade frauds.
• Orchestrated a data pipeline via On-Premise Control-M jobs
• Conducted performance tuning and troubleshooting of big data applications for enhanced efficiency.
• Led initiatives to ensure compliance with data governance and security standards across projects.
• Analyzed complex datasets to derive insights that informed strategic business decisions.
• Supported on Business as Usual (BAU) initiatives in the bank by delivering Data Quality Excellence on top of the existing framework.
• Troubleshooted project-related issues and prioritized project activities to reduce impact.

Project: Trade Fraud Risk Management

Client: Standard Chartered GBS

Description: Trade Fraud Risk Management (TFRM) aims at reducing the Trade frauds by creating client specific Fraud & Anti-Money Laundering (AML) scenarios by leveraging big data from multiple sources.

Technologies: Quantexa, Apache Spark, Scala, Python, HDFS, Hive, Kibana and Control-M

Roles and Responsibilities:

• Developed ETL processes using Apache Spark for efficient data transformation including extracting and parsing the data from the different internal and external sources.
• Developed scenarios to find out the risk customers and invoices over the fraud analysis.
• Collaborated with cross-functional teams to optimize data architecture and storage solutions.
• Played an integral role in the design and development of ingesting MANTAS India data from Standard Chartered's private Azure data lake into on-prem.
• Played a key role in the migration of ECDP -> Client Central, Hogan -> EBBS deliveries. Leveraged Quantexa’s ETL and scoring pipeline for solutioning. Liaised with cross functional teams in requirements analysis and documentation.
• Played a critical role in the design and development of adding multiple new interfaces to the existing Fraud Detection framework built with Quantexa to improve the bank’s ability to identify trade frauds.
• Orchestrated a data pipeline via On-Premise Control-M jobs
• Conducted performance tuning and troubleshooting of big data applications for enhanced efficiency.
• Led initiatives to ensure compliance with data governance and security standards across projects.
• Analyzed complex datasets to derive insights that informed strategic business decisions.
• Supported on Business as Usual (BAU) initiatives in the bank by delivering Data Quality Excellence on top of the existing framework.
• Troubleshooted project-related issues and prioritized project activities to reduce impact.

Project: Digital Transformation & Data Engineering Initiative

Client: Myanma Posts and Telecommunications

Description: MPT is the first and leading telecommunications company in Myanmar. Providing both fixed and mobile telecommunication services to people and enterprises of Myanmar. MPT BI-IOS is collecting data from different sources and transform them as per the business logic and storing into Hadoop File System. From Hadoop storage layer, generating reports, down-streams and up-streams by analyzing and computing data.

Technologies: HDFS, YARN, Apache Spark, Apache NiFi, Apache Hive, Ranger, Superset, Presto, Apache Kafka, Sqoop, PostgreSQL and Git Lab.

Roles and Responsibilities:

• Designed and implemented scalable data pipelines to enhance data accessibility and reliability.
• Collaborating with business analysts and technical leads to analyze business requirements and technical specs.
• Led cross-functional teams in developing data models that improved analytics capabilities.
• Designed Apache NiFi pipelines to process data from different sources and storing into Hadoop File System.
• Optimized ETL processes, reducing data processing time and improving system performance.
• Designed Data Pipelines using Apache Spark to consume the data from Apache Kafka topics and transform.
• Involved in writing Spark applications to generate Call Data Record related KPI’s.
• Involved in creating Sqoop jobs to import data from green plum (GP) to Hadoop File System.
• Communicating with internal/external clients to discuss specific requirements and expectations, managing client expectations as an indicator of quality.

Project: Healthcare Data Analytics

Client: Syncrasy Labs

Description: Syncrasy Labs is a Health Care based company which collected the data from all the nearby hospitals, patients and doctors related data across US. The purpose of this project is providing the physician details to the user based on their search queries and their previous health transactions and nearby locations.

Technologies: HDFS, Apache Hive, Apache Spark, StreamSets, Apache Ranger

Roles and Responsibilities:

• Collaborated with cross-functional teams to define data requirements and ensure alignment with business objectives.
• Designed StreamSets Data pipelines for efficient data transformation including extracting and parsing the data from the RDBMS sources.
• Involved in creating Hive Tables and Views to store the processed data.
• Involved in creating Ranger policies and applying row level filtering and masking on Hive data.
• Involved in maintaining log messages.

Client: CheckBac Inc.

Description: CheckBac is a low-cost alcohol monitoring product, where it produces the user data in daily basis in the form of JSON and this data will land in HDFS initially. Once the data is available in HDFS we load into Hive tables.

Technologies: HDFS, Apache Hive, HBase, Ranger, Java, MySQL, Tomcat, Eclipse, Maven

Roles and Responsibilities:

• Developed interface for WEB pages like user registration, login, registered access control for users depending on logins using HTML, JSP, and Java Script.
• Collaborating with business analysts and technical leads to analyze business requirements and technical specs.
• Involved in creating Hive tables and views.
• Involved in writing CRON jobs to auto remove/move files from HDFS.
• Involved in creating Ranger policies to restrict the user permissions.