Akshay Habbu
About Akshay Habbu
Akshay Habbu is a Lead Data Engineer with extensive experience in data engineering, currently working at Blueshift in Pune, Maharashtra, India. He has a strong background in managing large data processing pipelines and optimizing Spark workflows, having previously held positions at PubMatic and Blueshift.
Current Role as Lead Data Engineer
Akshay Habbu currently serves as the Lead Data Engineer at Blueshift, a position he has held since 2022. In this role, he is responsible for overseeing the design and implementation of complex data processing pipelines. His expertise in managing large-scale data systems is integral to the company's operations. He works in Pune, Maharashtra, India, where he leads initiatives that enhance data processing capabilities.
Previous Experience at Blueshift
Akshay Habbu previously worked at Blueshift as a Data Engineer from 2019 to 2020 and later as a Senior Data Engineer from 2020 to 2022. During his tenure, he contributed to the development of data processing solutions that improved the efficiency of data handling. His experience at Blueshift laid the groundwork for his current leadership role.
Experience at PubMatic
Before joining Blueshift, Akshay Habbu worked at PubMatic as a Big Data Engineer from 2016 to 2019. He began his career at PubMatic as a Software Intern for six months in 2016. His time at PubMatic provided him with valuable experience in big data technologies and data engineering practices.
Educational Background
Akshay Habbu studied Computer Science at Walchand College of Engineering, Sangli, where he earned his degree from 2012 to 2016. Prior to that, he completed his Higher Secondary Certificate (HSC) at Sangameshwar College, Solapur, in 2012. He also achieved his Secondary School Certificate (SSC) from Siddheshwar High School, Solapur, in 2010. His educational background has equipped him with a strong foundation in data structures and algorithms.
Technical Skills and Expertise
Akshay Habbu possesses a strong technical skill set in data engineering. He specializes in designing and implementing scalable data processing pipelines using Spark and Scala. He has practical experience managing data processing pipelines that handle approximately 75TB of data daily on a cluster totaling 45PB. Additionally, he has expertise in optimizing Spark workflows and is familiar with various technologies, including NSQ, tile38, Ruby, Rust, and Elixir.