• Built and scaled large-scale Ads (AdTech) Forecasting data platforms supporting advertiser campaign planning and audience reach estimation.

• Designed distributed data pipelines and high-dimensional analytics systems using Apache Beam, BigQuery, and Elasticsearch to process large volumes of ad request data and power real-time APIs.

• Developed time-series data pipelines and datasets used by machine learning teams to improve forecasting models for campaign delivery and performance.

• Architected billable delivery and financial data platforms for the Ads ecosystem, supporting billing, payments, reconciliation, and financial reporting workflows.

• Designed and implemented advertiser-facing Ads APIs, and optimized forecasting APIs executing complex aggregations to significantly improve latency and scalability.

• Designed and prototyped a Data Management Platform to manage Patient Visit recordings, Transcriptions, Medical Notes, and Machine Learning output using AWS and Python.

• Designed and prototyped Real-time speech-to-text service using AWS, GCP, Java and Python.

• Technically led engineering team and collaborated closely with Data Science team to design and Implement a Data Management Platform to convert Medical/Pharmacy claims and EHR data into Longitudinal Patient Record using GCP and Python.

• Implemented Machine Learning Library for working with Longitudinal Patient Record using PySpark, GCP and Python.

• Supported pilots for customers.

• Supported Legacy Machine Learning Pipeline using Airflow and Apache Spark.

• Implemented AppDynamics Performance Monitoring product suite in customers's Production and Test environments

• Accelerated product adoption by developing integration modules, tooling, and extensions for AppDynamics products using Java, Javascript and html