Neya Renganathan

• Developed scalable Python backend services and REST APIs powering dashboards that visualize enterprise data
discovery and classification results.
• Implemented backend logic for aggregated and detailed dashboard APIs retrieving campaign, application, database,
and schema-level discovery insights.
• Built feedback management APIs and database models enabling users to submit, update, and track feedback on
discovered personal data findings.
• Implemented asynchronous background jobs using Celery to trigger alerts when data discovery thresholds are
exceeded.
• Developed email notification workflows to inform stakeholders about sensitive data discovery events.
• Optimized database schemas and SQL queries for job processing, feedback tracking, and notification management.
• Integrated backend services with SAS Viya discovery outputs to ingest and process classification results into the
platform database.
• Implemented role-based access control (RBAC) and application-level filtering to ensure secure and authorized data
access.
• Collaborated with cross-functional teams following the Waterfall development model, supporting structured
delivery phases from requirement analysis to deployment.

• Developed a serverless backend system for a human data labeling platform supporting machine learning dataset
preparation.
• Implemented AWS Lambda functions in Python to manage annotation submission, audit sampling, rebuttal
handling, and final validation workflows.
• Built REST API endpoints using API Gateway integrated with Lambda to enable annotators and auditors to interact
with the system.
• Designed automated audit sampling logic that selects 20% of annotation records for quality review using scheduled
EventBridge triggers.
• Stored annotation images in Amazon S3 and maintained metadata, workflow status, and review results in Amazon
DynamoDB.
• Integrated Amazon SNS to publish audit notifications and rebuttal alerts to annotators and auditors through
event-driven communication.
• Implemented multi-stage validation workflows including annotator submission, auditor review, rebuttal response,
and science team validation.
• Configured CloudWatch logging and monitoring to track annotation uploads, audit sampling metrics, SNS
notifications, and workflow health.
• Improved system reliability by implementing batch processing, idempotent message handling, and structured JSON
logging for traceability.

• Designed and developed machine learning models to analyze development metrics across different stages of the
Software Development Lifecycle (SDLC).
• Built backend services supporting ML model integration and analytics workflows.
• Developed ML-based analytics systems that improved SDLC productivity insights by 40%.
• Implemented data preprocessing pipelines for training machine learning models using development data.
• Developed applications that estimate story points and engineering productivity metrics, enabling improved project
planning.

• Developed scalable backend applications using Python and Flask, building REST APIs supporting enterprise business
applications.
• Designed backend modules enabling workflow automation and application integrations.
• Participated in Agile Scrum ceremonies including sprint planning, backlog refinement, and sprint reviews.
• Optimized backend systems through database schema improvements and SQL query tuning.
• Improved application performance by 20% through backend optimization strategies.
• Reduced application load times by 15% through efficient data retrieval and database query optimization.
• Delivered incremental features in two-week sprint cycles following Agile development practices.