VENKAT N S

Leadership & Team Management

Risk Data Mart (RDM) – GCP | Banking

• Built an enterprise‑grade Risk Data Mart on GCP supporting Basel & IFRS9 model monitoring (PD/EAD/LGD).
• Developed scalable PySpark pipelines on Dataproc, processing multi‑million‑record retail and wholesale loan portfolios.
• Orchestrated end‑to‑end workflows using Cloud Composer (Airflow) with dynamic, configuration‑driven DAGs.
• Designed and implemented a layered data architecture: GCS → BigQuery (Curated Data Mart) → Reporting/Model Layers.
• Implemented complex banking business rules including default logic, cure/settlement identification, and product lifecycle transitions.
• Optimized Spark and BigQuery performance via intelligent partitioning, clustering, caching, and execution‑plan tuning.
• Built a comprehensive data validation and reconciliation framework enabling audit and regulatory readiness.
• Managed platform infrastructure using Terraform (IaC) and implemented Git‑based CI/CD across environments.
• Lead a diverse team, providing technical direction, quality oversight, and delivery governance across data engineering initiatives.
• Actively manage counselees and consulting team members, offering career guidance, performance coaching, and structured development planning.
• Drive capability building through mentoring, peer learning sessions, and hands‑on upskilling in cloud, PySpark, and modern data engineering practices.
• Foster a collaborative, high‑performing culture aligned with consulting expectations on communication, client handling, and delivery excellence.

1. UK Bank – Model Monitoring Tool. US Bank – End-to-End ModelOps Solution, Indian Bank – Regulatory Data Mart.

• Role: Architect and Key Developer
• Tools: GCP, PySpark, Airflow, and Qlik.
• Designed and developed a model monitoring framework for regulatory models.
• Built data pipelines using PySpark and orchestrated workflows with Airflow on GCP.
• Developed Qlik dashboards for model performance insights and automated validation checks.

2. US Bank - ModelOps solution.

• Role: Lead Data Engineer & Solution Architect.
• Tools: AWS (SageMaker, Tecton, Seldon, Lambda, S3).
• Built a custom Feature Store and implemented AWS SageMaker for model training.
• Used Tecton for feature store management, and Seldon for model deployment.
• Developed CI/CD pipelines for automated model deployment and monitoring.

Indian Bank - Building Regulatory Data Mart.

• Role: Data Architect and Lead Engineer.
• Tools: Netezza, CDP, Hive.
• Designed and implemented a regulatory data mart for compliance reporting.
• Used Netezza, CDP, and Hive to manage and process large-scale financial data.
• Automated data ingestion, transformation, and reporting workflows.
• Accomplished multiple tasks within established timeframes.
• Managed and motivated employees to be productive and engaged in work.

• Finance Data Hub (FDH)
• Role: Data Engineer
• Skills: Netezza, Snowflake, Python, SQL, ETL, Cloud Migration, and Automation.
• Executed transfer of data from Netezza to Snowflake with uninterrupted integration.
• Automated SQL conversion using Python to accelerate migration.
• Revamped data processing systems to increase scalability.
• ML Pipelines for Finance
• Role: Machine Learning Engineer
• Skills: Python, SQL, PySpark, ML Models, Fraud Detection, ModelOps.
• Developed fraud detection ML pipelines, improving accuracy and detection rates.
• Designed and implemented feature engineering for financial transaction data.
• Integrated ML models with ModelOps workflows for automated monitoring and retraining.
• Management Analytics Platform
• Role: Data Architect
• Skills: AWS, Spark, Snowflake, Data Lake, ETL, Big Data, and Cloud Infrastructure.
• Built a Finance Data Lake on AWS with Spark and Snowflake.
• Designed scalable data architecture for financial analytics and reporting.
• Developed ETL pipelines to integrate structured and unstructured financial data.

• Requirements gathering from customer and documentation of requirements as per Agile Scrum process standards
• Arriving at appropriate complexity points of the User Story
• Involved in front end GUI development using dojo framework
• Worked on programming in Core Java and Design Patterns (in Presentation, Business and Data layers)
• Worked on the Implementation of internationalization with Properties file and Resource Bundle
• Involved in front end GUI development and enhancement related tasks
• Involved in writing SQL Queries, Views and Triggers
• Worked on Programming in database with functions and stored procedures
• Redesigned the application and database for new requirements
• Involved in writing Unit test cases
• Involved in writing CSS styling files for JSP styling
• DataProbe is an investigative data analysis and decision support system, enabling analysts, researchers, investigators and other knowledge workers to rapidly perform complex, multi-step, iterative investigation on various types of detail data from different sources
• Company Overview: DataProbe is an investigative data analysis and decision support system, enabling analysts, researchers, investigators and other knowledge workers to rapidly perform complex, multi-step, iterative investigation on various types of detail data from different sources