Software Engineer @ Quotient |đź“ŤSan Francisco, CA
As a Software Engineer at Quotient Technology Inc., I design and develop nano-services for the company's digital promotions platform, using Java, SpringBoot, GCP, and GKE.
Designed and developed revenue generating RESTful APIs for the company’s digital promotions platform in Java/SpringBoot.
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Spearheaded the development and maintenance of a revenue-critical transactional API responsible for processing promotions redeemed by users, ensuring data accuracy and reliability.
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Played key roles in design and development decisions for a cloud based solution by effectively using tools such as Dockers, Google Kubernetes Engine, CloudBuild, PubSub, Redis MemoryStore, Compute Engine, ArgoCD, Spinnaker and monitor- ing tools such as Splunk and Grafana.
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Collaborated and co-ordinated efforts between a global team for the seamless migration of several services from On-Prem to the Cloud Computing solution.
Designed and developed from scratch a config-driven ETL data pipeline for reducing the volume of application-level metric logs produced by multiple jobs of varying formats.
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Single pipeline with an event-based listener to parse the logs from all jobs and combine these logs using aggregation techniques in Dataflow before writing them to the analytics layer - BigQuery.
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Created a Google DataStudio dashboard for automated summary reports and insights into the logs being stored.
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Achieved an 85% reduction in data volume which resulted in a 99% reduction in the cost of querying, retrieval, and storage of the logs due to the efficiency of BigQuery.
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Implemented using GCP tools - PubSub, Dataflow, BigQuery, DataStudio - in the Java SDK, and Maven.
Built services and tools using Golang for a North American Retail Giant’s B2C e-Commerce platform.
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Designed and developed from scratch an automated data migration framework and tool for easy migration, transformation and filtering of data between different data sources. The tool was used internally by all teams.
Supported Data Sources : CSV, JSON, MySQL, Postgres SQL, MS SQL, Cassandra, Yugabyte, Apache Kafka (with Avro encoding), Google PubSub, Eventhub, MongoDB, Elastic Search, Solr
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Enhanced the usually linear data pipeline to enable multiple data inputs and outputs as well as complex and arbitrary internal dataflows using graph theory.
