Vishal Chaudhary

Engineering Manager

New Delhi, Delhi, India10 yrs 10 mos experience

Most Likely To Switch

Key Highlights

10 years of experience in core development.
Expertise in real-time and batch processing.
Strong background in team leadership and management.

Stackforce AI infers this person is a Backend-heavy Fullstack Engineer with expertise in Data Science and E-commerce.

Contact

Skills

Core Skills

JavaApache SparkElasticsearchMysql

Other Skills

AzureCC++Coding LanguagesGo (Programming Language)GrafanaGraphiteHibernateKibanaLinuxMicrosoft WordOracle PL/SQL DevelopmentPlay FrameworkRBACRabbitMq

About

Graduated from Indian Institute Of Information Technology, Allahabad . Always had an interest in coding and was an active member in competitive coding in college solving Analytical challenging problem. - ~10 years of experience in core development and designing applications which serve result in real-time, batch and Big Data, based on Open Source and Java/J2EE Technologies also containing experience of Team Leading and Management due to my current role. - Worked on components using Elasticsearch with Kibana and kafka to solve many use cases. Worked on components using Apache Spark to process data for billions of entities. - Additional languages worked on GoLang, Python

Experience

Bharatpe

2 roles

Engineering Manager

Promoted

Oct 2024 – Present · 1 yr 5 mos · Gurugram, Haryana, India · Hybrid

Handling the core payments system of Bharatpe

Technical Lead

Feb 2023 – Sep 2024 · 1 yr 7 mos · Gurugram, Haryana, India · Hybrid

Razorpay

Lead Software Engineer

Apr 2022 – Feb 2023 · 10 mos

Sense

Lead Software Engineer

Mar 2021 – Mar 2022 · 1 yr · Bengaluru, Karnataka, India

Inmobi

Senior Software Engineer

Mar 2019 – Feb 2021 · 1 yr 11 mos · bangalore

Lookalike Modelling (Data Platform)
Identifying users who exhibit similar behaviour as a given seed audience
Worked closely with Architect to design complete system from scratch to allow scalability and error handling (HLD - LLD)
Developed a RESTful service to handle CRUD of lookalike segments and related business logic.
Developed end-to-end pipelines for aggregation and ingestion of lookalike segments.
Tech - Spring Boot, Java, Apache Spark, Azure, SQL, Graphite, Grafana, RBAC

Spring BootJavaApache SparkAzureSQLGraphite+2

Quikr

Software Engineer

Apr 2017 – Mar 2019 · 1 yr 11 mos · Bangalure

1) Backend-Driven HomePage Generation (May 2017 - July 2017) :
Generation of Component and Client-Based Backend Driven HomePages , that creates a full data-filled Response to give back to respective client (android / web/ msite/ ios/ qsAdmin) and caching the responses against service-city-client combination key in Redis for quick access to the pages.
Tech Used: Redis, Java, Mysql
2) Slotting and Auto-Assigning PUDO (March 2018 - June 2018) :
Created full pipeline of Slotting and Auto-Assigning Pick-Up Drop-Off Bikers based on the availability of the bikers in a particular Zone-TimeSlot-Day combination. Calculate Time to take by the bikers from previous order to next order and checking the availability for that Time-Window with buffer time.
Tech Used: ElasticSearch, Redis, Java, Mysql
3) Matching Engine Improvements (July 2018 - Present):
Matching Engine is responsible for generation of QuikEasy Leads for the SMEs whenever a user creates a need for any service in a particular city. Improvements in SMEs listings on our platform matching for the created need. Support for enabling SMEs to exclude locality/city/State from which they don't want any leads. Also support given for AMP pages for FE whenever need created on msite for faster reload also helping in google-seo.
Tech Used: RabbitMq, Java, ElasticSearch, Play-Actors

RedisJavaMySQLElasticSearchRabbitMq

Snapdeal

2 roles

Software Engineer

Jul 2015 – Mar 2017 · 1 yr 8 mos

Data Platform : Data Platform is a centralized computing system for collecting, integrating and managing large sets of structured and unstructured event data from disparate sources, such as Web, WAP, APP , Database transactional logs etc. Events data are passed via certain business layers to transform these event data to appropriate avros to be logged in avro topics. Post that, the result is stored in various data-sources from where it is provided to the user via API layers and Admin server.