Jeevan K Email and Phone Number

• Led the development and implementation of a scalable Azure Data Lake from scratch, enabling the FedEx financial sector to centralize and streamline data storage, resulting in a 30% reduction in data retrieval times for business users.• Created automated data pipelines using Azure Data Factory, significantly improving data processing efficiency and reducing manual intervention by 40%, thus allowing the team to focus on value-added analysis.• Designed and implemented secure, scalable data pipelines using Python and Apache Airflow on Azure, ensuring encrypted data collection, transformation, and transfer for cybersecurity threat detection and monitoring.• Built and optimized data architectures integrating SIEM platforms (e.g., Splunk, QRadar) with cloud-based data warehouses (e.g., Azure Synapse, BigQuery), enabling real-time threat intelligence analysis and high availability.• Ensured compliance with internal security policies and industry regulations (e.g., GDPR, CCPA) by implementing data encryption, masking, and secure access control mechanisms for sensitive cybersecurity datasets.• Leveraged Azure Synapse to optimize data storage and processing, which enhanced data retrieval performance by 25% and supported near real-time financial forecasting and reporting.• Built and orchestrated complex data pipelines in Databricks to process high-volume datasets using Delta live tables, integrating machine learning models for predictive analytics, leading to more accurate forecasting.• Partnered with cybersecurity analysts and data scientists to design data models and ETL workflows for advanced threat detection, risk analysis, and incident response, utilizing tools like Hadoop and Spark for big data processing.• Developed automated monitoring and validation frameworks for data pipelines, leveraging logging and alerting tools to identify bottlenecks and optimize system performance for reliable and efficient cybersecurity operations.

● Migrating an entire oracle database to BigQuery and using power bi for reporting.● Experience in moving data between GCP and Azure using Azure Data Factory.● Wrote scripts in Hive SQL for creating complex tables with high-performance metrics like partitioning.● Experience in building power bi reports on Azure Analysis services for better performance.● Used cloud shell SDK in GCP to configure the services Data Proc, Storage, and BigQuery.● Deployed application to GCP using Spinnaker (rpm based).● Designed and Co-ordinated with the Data Science team in implementing Advanced Analytical Models in Hadoop Cluster over large Datasets.● implemented automation workflows in XSOAR to handle phishing emails flagged by Proofpoint Email Security, significantly reducing incident response times and manual effort by automating repetitive tasks.● Work related to downloading BigQuery data into pandas or Spark data frames for advanced ETL capabilities.● Worked with google data catalog and other google cloud APIs for monitoring, query, and billing-related analysis for BigQuery usage.● Import the data from different sources like HDFS/HBase into Spark RDD and perform computations using PySpark to generate the output response.● Manage Azure Kubernetes Services (AKS) and containerized applications, optimizing resource usage and performance.● Practiced SOC operations, utilizing Falcon CrowdStrike for threat detection, malware analysis, and proactive threat hunting to improve overall incident detection and response capabilities.● Built and maintained secure data pipelines to process and enrich cybersecurity threat intelligence data from tools like Proofpoint and CrowdStrike, ensuring high availability and real-time analysis for incident response.● Integrated and optimized data flows between SIEM platforms and cloud-based data lakes using Python and Apache Airflow, enabling efficient ingestion and analysis of security logs for threat detection and risk assessment.

● Imported Data from Different Relational Data Sources like RDBMS. Teradata to HDFS using Sqoop.● Imported Bulk Data into HBase Using Map Reduce programs.● Perform analytics on Time Series Data that exists in HBase using HBase API.● Designed and implemented Incremental Imports into Hive tables.● Used Rest Apl to Access HBase data to perform analytics.● Worked in Loading and transforming large sets of structured, semi-structured, and unstructured data.● Optimized MapReduce Jobs to use HDFS efficiently by using various compression mechanisms.● Experience in Implementing AWS solutions using EC2, S3 and Azure storage.● Experience in working on Data Lake setup with Hortonworks and AWS team.● Experience in managing multi-tenant Cassandra clusters on public cloud environment - Amazon Web Services (AWS)-EC2.● Experience in AWS cloud environment on S3 storage and EC2 instances.● Developed ETL workflow which pushes web server logs to an Amazon S3 bucket.● Used Amazon Elastic Cloud Compute (EC2) infrastructure for computational tasks and Simple Storage Service (S3) as a storage mechanism.● Involved in collecting aggregating and moving data from servers to HDFS using Apache Flume● Written Hive jobs to parse the logs and structure them in tabular format to facilitate effective querying on the log data.● Involved in creating Hive tables, loading with data, and writing hive queries that will run internally in map reduce way.● Experienced in managing and reviewing the Hadoop log files.● Migrated ETL jobs to Pig scripts to do Transformations, even joins, and some pre-aggregations before storing the data onto HDFS.● Worked with Avro Data Serialization system to work with JSON data formats.● Worked on different file formats like Sequence files. XML files and Map files using Map Reduce Programs.● Migrating on-prem ETLs from MS SQL server to Azure Cloud using Azure Data Factory and Databricks

• Analyzed business requirements, facilitating planning and implementation phases of the OLAP model in Team meetings.• Participated in Team meetings to ensure a mutual understanding with business, development, and test teams.• Encapsulated frequently executed SQL statements into stored procedures to reduce the query execution times.• Created SSIS packages to implement error/failure handling with event handlers, row redirects, and loggings.• Managed packages the in SSISDB catalog with environments; automated deployment and execution with SQL agent jobs.• Involved in the design of Data-warehouse using Star-Schema methodology and converted data from various sources to SQL tables.• Designed complex data intensive reports in Power BI utilizing various graph features such as gauge, funnel.• Obtained user approvals from the client for the collected requirements to ensure similar understanding between development team and business.• Extensively utilized SSIS packages to create complete ETL process and load data into database which was to be used by Reporting Services.• Identified the dimension, fact tables and designed the data warehouse using star schema.• Developed Multi-Dimensional Objects (Cubes, Dimensions) using SQL Server Analysis Services (SSAS).