Deepika T Email & Phone Number

• Designed and set up an Enterprise Data Lake to support various use cases, including the integration of healthcare-specific data such as Electronic Health Records (EHR) and clinical data for analytics, processing.
• Maintained quality reference data by performing cleaning, transformation, and ensuring data integrity in a relational environment.
• Developed a Security Framework to provide fine-grained access to objects in AWS S3 using AWS Lambda and DynamoDB, ensuring adherence to healthcare data privacy regulations.
• Set up Kerberos authentication principals to establish secure network communication on the Hadoop cluster and tested HDFS, Hive, Pig, and MapReduce for new users.
• Performed end-to-end architecture and implementation assessment of various AWS services like Amazon EMR, Redshift, and S3, leveraging Hadoop ecosystems for big data processing.
• Implemented ML algorithms to analyze patient data and predict health outcomes, utilizing Kinesis Firehose and the S3 data lake for real-time data ingestion and storage.

• Implemented a scalable and efficient real-time fraud detection system to enhance security and prevent financial losses for a major banking institution, focusing on protecting customer accounts and transaction integrity.
• Utilized GCP Pub/Sub as a messaging backbone for real-time transaction data ingestion. Implemented Dataflow pipelines for stream processing to enrich and analyze incoming transaction data.
• Deployed machine learning models on AI Platform for continuous evaluation of transactional patterns and fraud detection. Continuous streaming of transaction data from multiple banking channels into Pub/Sub.
• Real-time data processing and transformation using Dataflow pipelines to derive actionable insights for banking operations.
• Integration of custom ML models for fraud detection and anomaly detection using TensorFlow/Keras. Automated alerting and reporting mechanisms for suspicious activities, directly notifying banking security teams.
• Leveraged Kubernetes Engine for container orchestration to ensure scalability and fault tolerance.

• Designed and implemented a real-time customer insights and fraud detection system for a leading bank using Google Cloud Platform (GCP).
• Utilized Google Cloud Pub/Sub to capture and ingest real-time transaction data, including deposits, withdrawals, and transfers from various banking channels.
• Implemented Apache Beam pipelines on Google Cloud Dataflow to process and transform streaming data in real-time, ensuring data was cleansed, enriched, and aggregated for fraud analysis.
• Stored processed data in Google BigQuery for scalable, high-performance querying and analysis, and utilized Google Cloud Storage for cost-effective storage of raw and processed financial data.
• Developed real-time analytics dashboards using Google Data Studio, powered by BigQuery, to provide actionable insights into customer transaction behaviors and detect potentially fraudulent activities.
• Built and deployed machine learning models using Google AI Platform to predict and detect fraudulent transactions, ensuring the security and integrity of customer accounts.

Hyderabad, Telangana, India

• Created and deployed custom PySpark scripts to preprocess and transform data, mapping it to tables within the CIF data warehouse.
• Implemented AWS Glue ETL jobs to automate the extraction, transformation, and loading of data from diverse sources into S3 and Redshift.
• Developed and managed shell scripts for Sqoop jobs to perform periodic incremental imports of structured data from various RDBMS to AWS S3.
• Utilized Kafka to ingest real-time website traffic data into HDFS, ensuring seamless data flow and availability for analysis.
• Translated complex code into Informatica logic, creating ETL design documents and developing mappings using transformations like Source Qualifier, Expression, and Lookup.
• Enhanced ETL pipeline performance by tuning SQL queries and implementing data partitioning techniques, reducing processing time and resource usage.