* Worked on migrating data pipelines from legacy in-house system to Azure Databricks, reducing data latency from 24h to 6h.* Designed and build data quality systems to proactively catch issues quickly, reducing user-reported issues by 60%.

* Designed and built source of truth fact and dimension tables and ELT infrastructure with Fivetran and DBT. Established best practices and conventions, enabling other teams to build their data marts. This enabled data freshness monitoring, common source of truth tables, CI/CD for data pipelines, and better data quality tests.* Designed and built data validation system using Great Expectations and k8s tasks to enable API-driven validation of data providing results via UI. This lead to a reduction in engineering hours spent from about 3 days per sprint to less than 15min work for the end user(non-engineer).* Updated product inventory ingestion data pipeline to use Snowflake instead of Postgres to get the new/updated product data. This lead to a reduction in data processing time from about 2hours to less than 10min.* Designed and built a data pipeline factory in Airflow, with config metadata that can be modified via REST APIs. This reduced engineering hours spent from 2 days per sprint to less than 10 min of work for the end user(non-engineer).* Worked on real-time data processing and enrichment of clickstream events in Apache Storm with AWS Dynamo DB to prevent over or under-spending on the allocated budget. This enabled other systems to have accurate up to date spend amounts.* Designed and built a cache data structure on Redis that enables fast lookup of bid metrics based on attributes of a click. The cache was refreshed via Airflow. This feature led to an increase in client spend by about 5mil in the first 2 months.* Designed and built a data pipeline to consolidate similar products without using unique ids, using Word2Vec, Spark, Snowflake, and Airflow. This enabled bidding on additional products per auction leading to an increase in spending of 9%.

* Designed and built data models for different types of TB scale data such as geolocation, clickstream, purchase, viewership data. * Designed and built data pipelines using Spark. This reduced data processing time from about 7h to less than 1h.* Designed and built APIs to enable application users to send data to multiple partners. This provided users with one central control platform instead of manually uploading data into the individual partner portal.* Worked on migrating data from AWS Redshift to properly partitioned dataset on S3. This enabled the use of AWS Redshift Spectrum reducing warehouse cost by about 80%.* Set up Apache Airflow for data pipeline orchestration and scheduling, leading to a reduction in data freshness and correctness issues by about 75%.* Worked on DSL to allow application users to join datasets visually. This leads to end-users being able to perform complex joins and aggregates.

• Led project to automate ETL pipelines for high availability of data and significantly reduced wait time for analyses.• Developed graph analysis algorithm to capture fraud signals resulting in savings of 5M dollars.• Designed and deployed an ML pipeline in Python to help clients take immediate action on possible fraudulent policies in the vehicle insurance industry.• Reduced the execution time of a query to <1s from 45s by de normalizing the data.

• Developing software for hedge fund clients focusing on commercial real estate sector, using C#, ASP.NET, Tortoise svn, MS SQL.* Developing custom applications using AngularJS

• Built a web application for MTA employees using Java, javascript and jetty servlet engine.• The web application incorporated multiple python scripts, oracle stored procedures and C++ executable files.• The application was built on hibernate framework.

• Worked on banking retail sector website using technologies such as Java, EJB, JSP and javascript.• Developed an automated error log system for the entire project using Java.log4j