Engineering Manager @ Delhivery | Strategic Planning, Building Scalable Product, Team Management, Hiring, End to End Delivery
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Assessing your cultural and operational fit
Lead Software Engineer with 8+ years of hands-on experience in technology, product, leadership, and people management. Experience in building scalable, high availability cloud-based systems and web applications using cutting-edge technology, and building and leading a team of engineers.
Jaypee Institute of Information Technology
Bachelor of Technology (BTech), Computer Science
January 1, 2012 – January 1, 2016
Delhivery
Engineering Manager
February 1, 2025 – Present
Gurugram, Haryana, India · On-site
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Engineering Manager
April 1, 2024 – February 1, 2025
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Technical Lead
June 1, 2022 – April 1, 2024
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Backend Lead Engineer
October 1, 2020 – July 1, 2022
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Senior backend developer
June 1, 2018 – October 1, 2020
Betaout
Machine Learning Engineer
June 1, 2016 – May 1, 2018
Noida Area, India
Internship at Indian Institute of Technology, Delhi
Research based live project for predicting doctor and nurses best suitable in AIIMS
May 1, 2015 – August 1, 2015
Formula Builder (Real time variable generation using stream)
November 1, 2017 – Present
Making new variables defined by client using existing variables and updating those variable in real time as soon as the event occur using kafka and storm. Tools/Technology Used Storm, Kafka, HDFS, Hive, Cassandra, Hbase, Scala, Zookeeper, ElasticSearch
User Predictive segmentation
July 1, 2017 – Present
Using Logistic and Survival Regression for segmenting users based on their engagement to maximize purchase conversions. It aims to solve use cases such as 'identify users with the highest intent to purchase', 'identify users which are expected to churn', “identify users who will purchase in next few days”. Later segment user on the basis of their predictive feature. Tools/Technology Used Python, Jupyter Notebook, Apache Spark
User Analytics using Elasticsearch
November 1, 2016 – March 1, 2017
Classifying user on the basis of their properties and making the cluster homogenious and identifing a subset that can represent the whole population. Graphical representation of ecommerce data for marketer. Showing relation and graph between the properties of the users. Making hypothesis so as to perform actions on users. Tools/Technology Used Elasticsearch, Python, RabbitMQ, MySQL, Docker
E-Commerce Recommendation Engine
June 1, 2016 – May 1, 2017
Built and deployed recommendation engine for e-commerce clients based on Correlated Cross-Occurrence, collaborative filtering using Universal Recommender (PredictionIO) which led to increased conversion. Implemented data pipeline for ingesting data into data store. Created APIs to accept data as HTTP request and formatted it into standard format before feeding into HBase. Installed and configured Ambari server for provisioning, managing, and monitoring Recommender Engine Ecosystem. Automated Infrastructure deployment using Docker and Ansible scripts which makes new instance deployment a single command task. Designed and implemented scalable architecture for Recommender Engine which can easily sustain heavy loads. Monitoring the whole system logs using Kibana. Tools/Technology Used PredictionIO, PHP Scala, Apache Spark, Redis, Elasticsearch, Apache Ambari, Apache, Hbase, RabbitMQ, Apache Hadoop, Apache Hbase, MySQL, Zookeeper, Docker, Filebeat, Logstash, Kibana
Research Intern at Department of C.S.E I.I.T Delhi :- (May 2015-July 2015)
May 1, 2015 – July 1, 2015
A Machine Learning based live project of All India Institute of Medical Science (A.I.I.M.S) under Professor K.K Biswas, IIT-Delhi. The live project involves training of weekly obtained parameters (Temperature, IBI, Heart Rate etc ..) of doctors and nurses to obtain a report whether a person is worthy for performing his/her duty on a particular day on a particular case. The system is fully automated and the data is trained using SVM, the project is made in python.
Interview Cracker
January 1, 2015 – Present
It is a Networking project (in a group of 4) which is divided into 3 phrases: MCQ ROUND: Server starts the contest all connected client can sends the answer text file to the server which returns their score and rank. CODING ROUND: When server starts the contest, client have to submit their code's text file to the server, which compiles and return whether the answer is correct or wrong or it have any other error. TELEPHONIC INTERVIEW: It's a voice exchange through IP between connected systems. The project is in python and database used is MYSQL DB.
FOOD-O-MANIA
April 1, 2014 – Present
It's a Web Based project (in a group of 4) with an Android application created using Html5, Php, CSS and JavaScript. It is restaurant chain as like dominos where we can order, reserve the table, check menu. Also we can get registered, login and then avail various discounts. MYSQL db is used for this.
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong inclination towards data-driven solutions and complex system development, aligning well with a Big Data Engineer role. The diversity of projects, from predictive analytics to real-time stream processing and e-commerce recommendations, indicates adaptability and a broad technical interest. However, the recent shift to Engineering Manager roles might suggest a move away from hands-on Big Data engineering, which could be a point of discussion regarding cultural fit for a purely technical Big Data Engineer position.
Soft Skills & Operational Fit
The candidate's progression into Engineering Manager roles suggests strong leadership, communication, and problem-solving skills. The project descriptions, while detailed, could benefit from more explicit articulation of challenges faced and solutions implemented, which would further highlight operational fit. The absence of psychometric test results limits the assessment of stress handling and team collaboration.