Atindra Das

CEO

Mumbai, Maharashtra, India14 yrs 8 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in low latency software development for trading systems.
Proven track record in architecting scalable software solutions.
Strong leadership in managing high-performance teams.

Stackforce AI infers this person is a Fintech expert with a strong focus on low latency trading systems and software architecture.

Contact

Skills

Core Skills

Java TechnologiesSoftware DevelopmentBig DataArchitectureE-commerce

Other Skills

Algorithmic tradingAlgorithmsApache SparkCassandraContainerizationCore JavaDisruptorDistributed SystemsDockerHadoopHiveJ2EEJavaJava web technologiesKubernetes

About

* Passionate leader and expert in broad technical areas: project management, systems analysis, architecture, software design and development, quality assurance, performance characterization, and release engineering. * Eight+ years of experience in large scale low latency software development in Systematic Trading (Algorithmic Trading), E-commerce, Commercial domain. * Top quality architect, expertise with different Java Technologies, Multithreading, Object-Oriented Design, and Big Data Technologies. * Post Graduate in Computer Application from IIT Delhi with strong background in Data structures and Algorithms.

Experience

Edelweiss financial services limited

3 roles

Principal

Promoted

Apr 2022 – Present · 3 yrs 11 mos

Deputy Vice President

Apr 2021 – Sep 2022 · 1 yr 5 mos

Senior Manager

Nov 2014 – Sep 2022 · 7 yrs 10 mos

As a Senior Manager, I lead the team in strategic and operational planning to achieve business goals by fostering innovation, prioritizing initiatives in new technology and coordinating the evaluation, development, deployment and management of current and future trading systems (infra).
TBT Analytic Service:
o Designed, implemented and maintained a service which is managed to receive high volume (2 million packets per second) real time market feed over network, process them and inject into self-managed Cassandra Cluster.
o Ultra-low latency Java technology like Disruptor, NIO direct buffers, memory map, selectors etc. used to speed up processing time.
o Application and Network layer (used Solarflare NIC) tuning has been done to avoid packet drops.
Backtesting Framework:
o This platform is used to simulate Directional and MN strategy on history data and simulation results are used in live trading and research.
o Market data of different geography (India, US, Japan etc.) and of different type like pricing, non-pricing, fundamental, etc. are stored in central Hadoop Cluster.
o Hive is used as data warehouse of all research and batch processing.
o Simulation runs in form of Spark job on those data and results are stored back to Hadoop Cluster.
o Lots of Optimization and tuning has been done to run 10K+ simulation on 20 years of data in 7 – 8 hours.
Order Execution System:
o Common service responsible for all order execution of live trading.
o Different standard execution logics like (TWAP, VWAP etc.) or Custom logics to execute Limit order, SL order, Options order, IOC order has been incorporated in this module.
o Continuous enhancement like SOR, Client level order aggregation etc. has been done on this product to reduce slippage which directly impact in revenue generation.
o All Executor Services are moving from monolithic to microservices architecture (Containerization, Kubernetes, Docker) for zero downtime on service failover and service management.

JavaCassandraHadoopSparkMicroservicesContainerization+6

Herald logic

Softwere Engineer

Sep 2013 – Nov 2014 · 1 yr 2 mos · Mumbai Area, India

Working with development team where I am responsible for end to end developing and delivering new commercial products/ tools using Core Java, Java web technologies, Oracle Database and improving/enhancing existing one’s as per requirement of client.

Core JavaOracle DatabaseJava web technologiesSoftware Development

Rediff.com

Product Engineer

Jul 2011 – Aug 2013 · 2 yrs 1 mo · Mumbai Area, India

Recommendation Systems:
Responsible to analyze, design and develop different recommendation systems for Online Shopping (E-Commerce) and Online Video Watch (I-Share).
Correlated Category based Recommendation:
o This recommendation is generated on product’s category level learning from user action.
Complementary(accessories) Category based Recommendation:
o Accessories and complementary category products of electronics goods are recommended here.
Collaborative Filtering based Recommendation:
o Item to Item based collaborative filtering algorithm on viewed products (also viewed).
 Techniques Used: Core Java, Multithreading, Object-Oriented Design and J2EE.
Text Spam Filter:
Text Spam Filter for Message Board where we use Naive Bayes Classifier and some text matching algorithms to detect spam.
 Techniques Used: Core Java, J2EE and Machine Learning.
Along with above I was also responsible for the following,
Analyzing users log and trying to find some new Pattern and Users Behavior for developing better Recommendation System.
Research based study for searching new approach and algorithm in the field of Recommendation.