Play Games 24x7 - Senior Data Engineer - ETL/Machine Learning (5-7 Yrs) Bangalore (Systems/Product Software) by PLAY GAMES 24X7

XpatJobs (November-2017 से पंजीकृत) ने 10 दिन पहले इस नौकरी को पोस्ट किया था

About Play Games24x7 : We are Indias largest gaming company, in terms of revenues AND the diversity of our product portfolio. We operate some of Indias most popular card games such as rummycircle.com, ultimate-teen-patti and ultimate-rummy. At the cusp of a breakout in the already-explosive growth rates of gaming industry in India, we are looking to diversify into a few more real-money gaming sectors, create more titles and other casual games. We are passionate about games and not surprisingly, are very opinionated about what constitutes good game design and user experience. We never shy away from trying new and radical stuff, but we calibrate our aggressive innovative spirit with measurement that inevitably humbles us, teaches us and always improves our ability to deliver an awesome playing experience. Our operating ethos as a company, irrespective of the product, is grounded deeply in what our players tell us with every click of their mouse. We analyse enormous amounts of data to understand our players- likes/dislikes, and constantly evolve our machine learning models to continuously improve their playing experience. Why Play Games24x7 : - You will do some of the most challenging work of your career at Play Games24x7. Games are typically low-latency, high throughput, and highly available applications.
-You will need to dig deep, to create a highly scalable, extensible and available distributed systems infrastructure, if you are a back-end engineer.
-Or a pixel-perfect delightfully rendered pwa, apk, ipa with a very smart set of client-server communication channels, if you are an app developer. Or a really slick set of deep-learning models to achieve a safe and clean experience for our users, if you are a data scientist. At Play Games24x7, you can innovate, take impactful decisions, move fast and be transparent. You are set free, as you strive to put a smile on each of our users faces. Role Description : - You will create ETL and ingestion jobs for the modelling pipelines for various machine learning/deep learning modules.
-Being a part of the Data-Science team at PlayGames, you will work with some of the very accomplished data scientists in the industry to help them create models for intent mining, churn prevention, fraud detection, behaviour prediction etc.
-You will be responsible for the evolution of an efficient BI pipeline into a multi-faceted pipeline to support various modelling needs.
-You will be responsible for the cost-efficiency/time-to-market tradeoffs while considering multiple tech choices for the problem at hand.
-You would ideally have a great knack for associating with the end use-cases for which the models are being created, to be able to opine intelligently about cleansing of data, extraction of features and schemas in the intermediate stores, before ingesting the voluminous amount of data into the modelling pipelines. Requirements : - B.E/MS in Computer Science or equivalent.
-5 years or more of progressive software technology experience with at least 3 years in an data engineering role.
-Ability to Plan, Design, Implement and Optimize BI solutions in SQL / NoSQL environment using best BI practices - Proficiency in writing ETL jobs - Ability to write complex queries for SQL/noSQL datastores such as document stores, columnar stores or graph stores - Great repertoire of knowledge in existing big data technologies such as Hadoop, Hive, Pig, Spark, Storm etc.
-Objective decision-making framework wrt tradeoffs between time-to-market and cost-efficiency - Huge interest in learning different algorithms and models used for machine learning - Keen eye for making mundane big-data jobs automated - Monitor and Improvise best practices / processes to manage daily BI operational work - Building quick PoCs and full-fledged solutions with various AWS managed services would be a big plus.

Required Skills : English