About

Sunando Bhattacharya is a collaborative engineering professional with over 7 years of experience in designing and executing complex business problems involving large scale data. He has a strong background in building highly scalable, robust and fault tolerant systems. Currently working as a Spark Engineer at Databricks, Sunando has extensive experience in the Big Data Ecosystem, including HDFS, YARN, Map Reduce, Apache Spark 2.2, Hive, and Oozie. He is also proficient in SQL Technologies such as Teradata, DB2, SQL Server, and Oracle 10g, as well as NoSQL Technologies like MongoDB. Sunando has a diverse programming language skill set, including C, C++, Java, Python, and C#. He is also experienced in ETL Tools such as SAS Data Integration and Real Time Data processing with Apache Spark Streaming. His proficiency in operating systems includes Redhat, Ubuntu, and MS Windows, and he has used cloud services such as EC2 AWS Instances and Azure WebApps. Additionally, Sunando has experience in web technologies, including ASP.NET, ASP MVC, and ASP Web Api. In his previous roles as a Data Engineer at JP Morgan and an Application Development Analyst at Accenture, Sunando was responsible for designing and developing data load frameworks, ingestion workflows, and cluster to cluster data ingestion frameworks. He also migrated ingestion and data engineering jobs from traditional ETL tools to Hadoop native ecosystems and developed Apache Spark-based generic ingestion frameworks for RDBMS systems. Sunando holds a Bachelor's Degree in Computer Science from KIIT University and completed his ICSE from Delhi Public School Newtown. He is proficient in a wide range of tech stacks, including Spark, Databricks, SQL, Data Engineering, AWS, Java, Web, HDFS, Big Data, ETL, Hadoop, Apache Hive, dotNET, ASP.NET, NoSQL, MongoDB, Azure, Integration, Python, Kafka, Jenkins, and UI. With his relevant experience of 7.45 years, Sunando is a skilled and dedicated professional who can effectively design and execute complex business problems involving large scale data.