Karthikeyan Rajasekaran

• Company Overview: Zelle is a digital payments network run by a private financial services company owned by conglomerate of banks. The Zelle service enables individuals to electronically transfer money from their bank account to another registered user's bank account (within United States) using a mobile device or the website of a participating banking institutions.
• Leading a diverse and innovative team of Data Engineers, Business Intelligence Engineers and managers to deliver high-value Data and Analytics solutions.
• Developed and implemented business intelligence strategies to enhance operational efficiency and revenue growth.
• Directed cross-functional teams in executing data analytics projects aligned with organizational objectives.
• Spearheaded the adoption of advanced analytics tools, such as Quicksight, Tableau, and ELK stack, to improve data visualization/reporting and observability.
• Build the service layer for the data engineering platform catering to the needs of the downstream systems in Java and Scala.
• Re-engineered the end-to-end Data pipelines, modernized & simplified Datasets, removed redundancies and achieved 40% reduction in Infrastructure & engineering cost.
• Worked on establishing the team to build the enterprise data platform which zelle uses to apply the calculations across their transactions.
• Built the Data Engineering team and Quality Engineering team to cater the needs of Zelle product and the associated upstream and downstream applications.
• Delivered self-service analytics solutions for organizational management reviews and partner teams by enabling single source of truth data store and analytics packaged by building an obfuscation framework for masking the PII data.
• Improved the operational excellence and data availability of various KPIs required for senior level Weekly and Monthly Business reviews.
• Zelle is a digital payments network run by a private financial services company owned by a conglomerate of banks. The Zelle service enables individuals to electronically transfer money from their bank account to another registered user's bank account (within the United States) using a mobile device or the website of a participating banking institutions.
• Environment: Python, Scala, Java Spring Boot - micro services, REST API, PyTorch Lightening, Spark, AWS Glue AWS Lambda AWS Athena, AWS Dynamo DB, EMR, DBT, Airflow for orchestration, Apache Kafka and Pyspark for Onprem processing and PowerBI as reporting tool.
• Created multi-site system architecture plans to reduce redundancy across entire organization.

Company Overview: AAA started as a federation of motor clubs throughout North America. AAA is a privately held association and service organization which provides various services to its members. AAA Life is an insurance service segment with AAA and they wanted to build their lakehouse porting their bigdata platform into AWS.

• Led enterprise-wide initiatives on cloud transformation, ensuring the rapid modernization of the data platform using Snowflake.
• Enforced industry standards by following best practices such Code Versioning, CI & CD, Automated testing, introduced change management process and Code Deployment thereby achieving operational efficiency and improving time to market.
• Involved in end-to-end architecture, strategy, roadmap, design & implementation of various Data & analytics solutions for Customer & Merchant Marketing teams.
• Lead the team to establish the AI/ML platform for AAA to run the model against their customer data.
• Architected the AWS data migration to the three layered medallion lake house architecture to cater the need for processing the various incoming data from different customers.
• Architected the AWS infrastructure demarking the needs for each service and planned the overall migration from onprem HDFS clusters to Amazon S3.
• Led & Managed scrum teams spread across US and India, catering to the Business Intelligence & Data Engineering needs of various business groups in the Marketing organization.
• Arrived at a solution with AWS suite of services involving AWS Glue, S3 buckets, Apache Iceberg, AWS Athena, DynamoDB, AWS Lambda and Step functions.
• AAA started as a federation of motor clubs throughout North America. AAA is a privately held association and service organization which provides various services to its members. AAA Life is an insurance service segment with AAA and they wanted to build their lakehouse porting their bigdata platform into AWS.
• Environment: Snowflake, Python, TensorFlow, AWS Sagemaker, Java Spring Boot, Spring Batch - micro services, DBT, REST API, Spark & Scala, Snowflake, AWS Glue, AWS Lambda, AWS Athena, AWS Dynamo DB, EMR, Apache Airflow, Apache Iceberg, Solace messaging, Apache Kafka and Pyspark for onPrem processings, GCP, DataProc and GKE with Microstrategy as reporting tool.

• Company Overview: Barclays is a British multinational universal bank, headquartered in London, England. Barclays operates as two divisions, Barclays UK and Barclays International, supported by a service company, Barclays Execution Services. We were part of the key technology consultants in architecting the data platform for the risk assessment.
• Led a team of data engineer to build the risk analytics data platform to implement the enterprise level risk management and implement strategies to enhance efficiency of risk managers across the organization.
• Involved in end-to-end architecture, strategy, roadmap, design & implementation of various Data & analytics solutions for Risk assessment.
• Directed cross-functional teams in executing risk analytics projects aligned with organizational objectives and involved in establishing the right architecture for each component.
• Managed the team to build the Risk Advisory data management ONERISK platform, a strategic solution to consolidate all the asset class risk data under a single data mart. This is an Actuarial Risk assessment platform which helps the risk manager to identify and mitigate the risks. The solution was built on AWS with Spring Boot on the service layer, and Scala and Spark as the programming languages for the calculation layer with ElasticSearch as the data cache/ search engine with Micro Strategy as the BI tool.
• Enforced industry standards by following best practices such Code Versioning, CI & CD, Automated testing, introduced change management process and Code Deployment thereby achieving operational efficiency and improving time to market.
• Improved data quality checks and help reduce the issues in weekly & monthly reporting by 25%.
• Developed Spark-based ETL/ELT pipelines using Python, Scala, or SQL and implemented Delta Lake and enabled advanced analytics with Databricks MLflow, AutoML, and Structured Streaming.
• Introduced the features of Photon Engine and tuned it to cater the needs of Databricks Lakehouse Platform.
• Introduced the three layered Medallion architecture to build the datalake for the Portfolio analytics platform.
• Created the datamodel using DataVault and Kimbell methodology.
• Barclays is a British multinational universal bank, headquartered in London, England. Barclays operates as two divisions, Barclays UK and Barclays International, supported by a service company, Barclays Execution Services. We were part of the key technology consultants in architecting the data platform for the risk assessment.
• Environment: PyTorch, Azure Databricks, Azure DataFactory, DataLake, Java Spring Boot, Spring Batch - micro services, REST API, pySpark, TensorFlow, Spark & Scala, ElasticSearch, Snowflake, Redis, Gridgain, Apache Airflow, Solace messaging, Apache Kafka, Pentaho suite, ReactJS and AngularJS, AWS, Jenkins for CI/CD and Stash for configuration management, GCP, Dataproc, Dataflow, Looker, BigQuery, Bigtable and GKE for containerization and PowerBI and Looker as reporting tools.

• Company Overview: NatWest Corporate and Institutions provides financing and risk management to UK and Western Europe customers and trades with relevant financial investors. We were part of the key technology consultants in architecting the data platform for the risk assessment.
• Led enterprise-wide data governance initiatives, ensuring data integrity and security across multiple platforms.
• Directed cross-functional teams in executing risk analytics projects aligned with organizational objectives and involved in establishing the right architecture for each component.
• Managed the team to build the Risk Advisory data management platform to calculate VaR and FRTB for a leading bank in UK. The solution was built on Google Cloud Platform with Spring Boot/Spring Batch and Akka framework on the service layer, and Scala and Spark as the programming languages for the calculation layer with Dataproc and Big Query as the data warehousing with Hive on-prem.
• Build the metadata based Service Mesh to integrate the applications across the risk management portfolio and managed the deployment of the apps using OpenShift.
• Increased the adoption of Risk analytics platform by improving user base from 250-1000+ user in 1-year span.
• Worked closely with Business and Technology stakeholders, source system teams and various internal and external customers to implement highly scalable, robust, and secure Data Engineering and Risk Analytics solutions.
• Lead the team to build the set of applications in reconciliation where the system will help in reconciling the book level, measure level VaR, PnL Vectors. Immense knowledge on VAR calculations-sensitivities based approach & internal model approach. Worked in developing a meta data driven platform for orchestrating the Var/FRTB calculations -SA approach, Default Risk Charge, Expected Shortfall, Stressed ES. The solution was built on GCP with Spring Boot on the service layer with Oracle as the database for maintaining metadata and event driven calculation using Apache Kafka. The calculation tier was built on the Cloudera environment with Apache Spark for processing.
• NatWest Corporates and Institutions provides financing and risk management to UK and Western Europe customers and trades with relevant financial investors. We were part of the key technology consultants in architecting the data platform for the risk assessment.
• Environment: Spark & Scala, pySpark, Apache Hive, Apache Kafka, CDP, Java Spring Boot and Spring Batch, GCP - Dataprocs, Big Query, Snowflake, GKE, Terraform, HDFS, Apache Kafka, Java, Pentaho suite, Apache Airflow, Web Services, Openshift Kubernetes, TeamCity CI/CD and PowerBI as reporting tool.

• Company Overview: Premier Inc. is a healthcare insurance company uniting alliance of approximately 4,350 U.S. hospitals and health systems and more than 300,000 other providers and organizations. As industry leader, Premier has created one of he most comprehensive databases of Actuarial risk data, clinical best practices and efficiency improvement strategies. GAVS is one of their strategic IT partner providing their end to end IT solutions.
• Led a global team of 50+ Data engineers, analysts, BI professionals, and Project managers located in 6 different cities in US, Saudi & India; Worked across several cross functional teams, created, and executed roadmaps, POCs, Designed & Implemented several high value Data engineering & analytics solutions for BSF and PREMIER.
• Collaborated with IT & Business user community to implement multiple finance, compliance, and risk initiatives.
• Led team to implement PowerBI across enterprise.
• Derived project scope, timelines, and deliverables for Power BI implementation (e.g., data connections, dashboard development, security setup, user training).
• Reviewed Power BI architecture design, including data gateways, datasets, and workspace configurations.
• Established data governance practices (row-level security, data lineage, compliance with GDPR/CCPA) are embedded in Power BI solutions.
• Managed end-to-end project lifecycle from submitting proposals for project, allocation of resources, execution of projects and tracking status, and weekly status reporting to key stakeholders all while being Senior Architect designing solutions for platform team was tasked to complete.
• Hosting delivery for establishing Predictive Analytics platform for bank using Apache Spark and Kafka Streaming and BigData platform.
• Brought in right architectural principles to bridge gap in existing technical stack.
• Started with project initiation, planning, budgeting, resourcing and execution.
• Worked with business closely on drawing business plan and road map for delivery.
• Involved in identifying opportunities with account managers and converting them into proposals.
• Established bridge across upstream and downstream systems to make communication smooth throughout deployment process.
• Has brought in execution excellence across organization being part of organization transformation strategy team.
• Premier Inc. is healthcare insurance company uniting alliance of approximately 4,350 U.S. hospitals and health systems and more than 300,000 other providers and organizations. As industry leader, Premier has created most comprehensive databases of Actuarial risk data, clinical best practices and efficiency improvement strategies. GAVS is one of their strategic IT partner providing their end to end IT solutions.
• Environment: Spring Boot, AWS S3 and EC2, low latency computing with Spark, used Scala for programming, HDFS, Informatica Power Center AND DBT for ETL, Java, Web Services, SOAP/ REST, MS SQL Server 2014, PowerBI and Tableau as reporting tool.

• Company Overview: Levi & Strauss American clothing company known worldwide for its Levi's brand of Jeans, wanted to implement predictive analytics and master data management (MDM) by change management for master/key data sets at Levi Strauss & Co., starting with product and store location data. This includes developing and implementing reporting capabilities, MDM capabilities, hierarchies, managing & improving data quality for master data attributes architecture and the technical solution for their SAP suite of applications.
• Led ETL & BO development team of 30 plus associates spread across 5 locations in India & US. Helped various customers in regulating DWH Environments, delivered several critical compliance initiatives such as Banking regulatory reporting etc.
• Started with project initiation, planning, budgeting, resourcing and execution.
• Worked with business closely on drawing the architecture for the BI and Data Analytics platform.
• Defined project scope, timelines, and deliverables for Databricks implementation (e.g., data ingestion, ETL workflows, ML pipelines, reporting dashboards).
• Translated technical capabilities of Databricks (e.g., Delta Lake, Spark, MLflow) into business value for stakeholders.
• Ensure Databricks architecture aligns with cloud infrastructure (AWS/Azure/GCP) and integrates with existing tools (Snowflake, Tableau, etc.).
• Migrated complex legacy platforms to EDW in alignment with the organizational goals.
• Built teams ground up, involved in hiring, mentoring, training new hires of various experience levels.
• Implemented various best practices in team that enhanced operational and SLA performance of various Data pipelines and reports.
• Lead data ingestion team which build the systems to inject data into Hadoop platform using Spark streaming transferring to Hive tables.
• Managed team which built ETL data pipelines and scheduling jobs with Spark/Scala and processing of data with transformation, validation and aggregation through Spark.
• Implemented best practices for improving data observability, data governance, and security for PII data at rest and in-flight.
• Identifying business attributes which needs to be rehoused into MDM from legacy application and deriving the technical solution and integrating it with existing SAP BW systems.
• Created specifications, data diagrams and technical design documents.
• Establishing of Data Quality and Quality measurement process to track consistency of Master Data.
• Maintained a close relationship with business team establishing a clear communication procedure involving timely formal reports and tracking status.
• Managed onsite resources with motivation during the highly pressurized situations and have driven delivery successfully during period of uncertainties.
• Owned Risk Management.
• Have taken care of end to end release management.
• Part of CENTRE OF EXCELLENCE(CoE) TEAM for business intelligence and data management.
• Groomed trainees on ETL, Oracle, DWH & basic UNIX Concepts.
• Have developed solution for Market Basket Analysis for the e-commerce website for Levi using Apache Spark using various transformations.
• Identified and fixed straggler jobs and automated straggler job identifications.
• Have developed a separate solution for converting other reports into Tableau visualization using Web Services, SOAP/ REST.
• Levi & Strauss American clothing company known worldwide for its Levi's brand of Jeans, wanted to implement predictive analytics and master data management (MDM) by change management for master/key data sets at Levi Strauss & Co., starting with product and store location data. This includes developing and implementing reporting capabilities, MDM capabilities, hierarchies, managing & improving data quality for master data attributes architecture and the technical solution for their SAP suite of applications.
• Environment: Spark and Scala for Predictive Analytics, Hive as no sql database, CDH, Hadoop MapReduce, HBase, Parquet, Impala, Databricks, SAP Business Objects for reporting, Informatica Power Center, Informatica Data Quality, Data Profiling, Java, Web Services, SOAP/ REST, MS SQL Server 2014, Tableau as reporting tool, along with statistical tool R and RStudio implemented in Hadoop.