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Exclusive Interview With Amogh Raghunath

In this article, we are going to interview Mr. Amogh Raghunath who works at amazon as an SDE- II.

Q1. Please tell us about yourself and where are you from?

My name is Amogh Raghunath, I was born in Bangalore, India. I spent about 25 years of my life traveling across various parts of India. Until 2015, when I decided to come out of my comfort zone to explore opportunities around me by taking up maters’ degree in Data Science from WPI. Since my childhood, I was fond of computers and learned programming at an adolescent age. Curiosity about how things are built in the computer world and bringing them to life always fascinated me.

Q2. What inspired you to embark on your journey into a data geek?

Before diving deep into my journey, I would like to tell you a bit more about my background. I have a bachelor’s degree in Computer science from India, and since my undergrad days after taking a data mining course, the role that data and its applications are playing in making huge company decisions has always excited me.

So, I decided to do my thesis in data mining under the guidance of my professors both at the undergrad and graduate levels. This is how I embarked on my journey toward the data world.

Q3. Please tell us about your experiences in building products that are data related. 

I currently work with Audible, an Amazon company, Newark NJ in the capacity of Software engineer building data products. My day-to-day responsibilities include designing/blueprinting data platforms that are critical in solving various data science/business intelligence and marketing needs. I use a lot of Amazon web services components to make this happen; to name a few I use lambda for service calls, s3 for data lake, containers for batch jobs, and glue ETL for the crunching of data into meaning information, etc.

Typically, the data ranged from TB to PBs scale type. So, scaling the application that we are building becomes crucial here. So, we always start with the functional and non-functional requirements before we even consider scaling the application to a variety of sources/teams. Typically, the use case of such systems helps solve a marketing problem. For example, if a company must run the marketing campaigns to target prospects, they need data in a near real-time manner, so the systems that I built help us achieve data in such latency.

Before Audible, I was working for a relatively small startup company in Boston, MA, where I was placed in a similar capacity. 

Q4. Please tell us about the refugee tool that you build for LIRS?

Sure, during my master’s degree at WPI I was fortunate enough to work with Prof. Andrew Trapp on a research project that involved.

I started working in the refugee space, at LIRS—a refugee resettlement agency—and I started adding collaborators such as my fellow teammates from WPI, and we looked at the problem of how do you place refugees well? Operationally, they need to place the refugees that they receive through the United Nations High Commissioner for Refugees and the U.S. State Department. So, these are already approved refugees. Where should they go throughout the country? Presently, it’s a manual process, which is a whiteboard and people sitting in a room every week.

So, to automate the process, we used a linear 0-1 optimization problem that would consider various characteristics of a refugee. For example, His Nationality, Ethnicity, Single Parent? and so, based on these parameters the tool would give weight to these cases and look up a back-end database in real-time to see which affiliate the refugee should be placed under. Not just that, it also provided the top three suggestions for the refugee.

The manual process of placing the refugees would take hours to days, got reduced to mins. I believe this was a great win for our team and critical especially in the times when there is a refugee crisis around the world.

To learn more about the tool: you can see this video

Q5. Lastly, what advice would you give to aspiring data geeks around the world?

My advice to aspiring data geeks is quite simple. Don’t ever stop learning, reading, and gaining that code fluency by grinding LeetCode or any competitive programming sites, this would leverage your profile to a great extent. It’s likely that you will always find someone that needs your help, and you could be an asset to any type of organization.

To learn more about Amogh, please visit his LinkedIn profile:

To learn more about his work please take a stab at his git profile:

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