Govind Agarwal

Senior Software Engineer

Seattle, Washington, United States9 yrs 10 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in AWS technologies and graph databases.
Proven track record in optimizing complex queries.
Strong foundation in machine learning and data processing.

Stackforce AI infers this person is a Backend-heavy Fullstack Engineer in the E-commerce industry.

Contact

Skills

Core Skills

Distributed SystemsSoftware DesignSoftware DevelopmentGraph Database SolutionsDeep LearningNlpData ProcessingData VisualizationData Management

Other Skills

MXNetNERLambdaStep FunctionsKinesis Data AnalyticsKinesis Data StreamsKinesis Data FirehoseGlueAthenaSQSSNSS3DynamoDBCloudwatchElasticSearch

About

Experienced Software Engineer with a demonstrated history of working in the IT industry. Skilled in Java, Python, Data Structures, Algorithms, ML, NLP & AWS technologies. Strong engineering professional with a Bachelor of Technology (B.Tech.) focused in Computer Science from Indian Institute of Technology (Banaras Hindu University), Varanasi.

Experience

9 yrs 10 mos

Total Experience

4 yrs 11 mos

Average Tenure

6 yrs 8 mos

Current Experience

Amazon web services (aws)

2 roles

Senior Software Engineer

Promoted

Apr 2022 – Present · 4 yrs 2 mos · Seattle, Washington, United States · On-site

Responsible for Query optimizations for openCypher, Gremlin and SPARQL.

Distributed SystemsSoftware Design

Software Development Engineer ||

Sep 2019 – Mar 2022 · 2 yrs 6 mos · Seattle, Washington, United States · On-site

Working on developing robust and scalable graph database solutions.

Software DevelopmentGraph Database Solutions

Amazon

3 roles

Software Development Engineer II

Apr 2018 – Sep 2019 · 1 yr 5 mos · Bengaluru, Karnataka, India

Currently working on a project that scrapes Amazon catalog and tries to find potential information errors like cross-attribute mismatch, image mismatch via Deep Learning, MXNet and NER.
1. Extended the whole architecture to support multiple clients with no client affecting the other client. This involved re-architecting the whole system for the new feature as well as to make it scalable.
Technologies Used: Lambda, Step Functions, Kinesis Data Analytics, Kinesis Data Streams, Kinesis Data Firehose, Glue, Athena, SQS, SNS, S3, DynamoDB, Cloudwatch, ElasticSearch, Redis Cache.

Deep LearningMXNetNERLambdaStep FunctionsKinesis Data Analytics+12

Software Development Engineer

Jun 2016 – Mar 2018 · 1 yr 9 mos · Bengaluru, Karnataka, India

Worked on developing and scaling a system named AutoASIN that auto-corrects vendor input based on a combination of historical data based learning and static business rules. Key technologies used are NLP, Hadoop, EMR and Graph-based supervised learning. Following are my major contributions to this project.
1. Developed a mechanism to correct brand value shown on detail page via historic data.
2. Developed a separate pipeline to unblock asin creation due to unnecessary errors. These items are then enriched after asin creation through manual intervention.
Worked on a project that scrapes Amazon catalog and tries to find potential information errors like cross-attribute mismatch, image mismatch via Deep Learning, MXNet and NER.
1. Developed a scalable reporting module for an intake of 100M events/day. This uses Firehose with Redshift to store the data such it is easy to build dashboards.
Technologies Used: S3, DynamoDB, Lambda, Kinesis Data Streams, SQS, RPC, Cloudwatch, Redshift, Kinesis Firehose Streams.

NLPHadoopEMRGraph-based supervised learningS3DynamoDB+7

Software Developer Intern

May 2015 – Jul 2015 · 2 mos · Bengaluru, Karnataka, India

Built an end-to-end infrastructure to provide insights into network monitoring data via visual dashboard on OBIEEE. This includes the following tasks:
1. Consolidating all the data distributed across multiple nodes in different geographical locations to a centralized location which in our case was S3.
2. Use Redshift to store this data into a format optimized for querying.
3. Built OBIEE dashboard for visual capabilities.
Technologies Used: Java, Distributed Job Scheduler, AWS S3, AWS Redshift, OBIEE.

JavaDistributed Job SchedulerAWS S3AWS RedshiftOBIEEData Visualization+1