Capital One on the Importance and Benefits of ‘Cross-Training’ Your Workforce

Originally published at Capital One Financial ranked No. 28 on The DiversityInc Top 50 Companies for Diversity list in 2021.


Can a data engineer become a data scientist and vice versa? How can you grow your skills to become a well-rounded data professional? Even if you’re an experienced data professional, you may still want to expand your knowledge and stretch in your career. For Sreekanth, training opportunities and a culture of support helped him grow from data analyst to data engineer.

Sreekanth was in a career advancement program that helps create well-rounded data professionals. “At Capital One, we have different data specialists, like data analysts and data engineers,” he says. “But the work overlaps in meaningful ways.”

To address that gap, Capital One data leaders created a learning path to support all of the different data specialists in the company. It offers relevant skills programming to cross-train engineers and analysts to become experts in all things data. And it all takes place during regular business hours!


Customized training, tailored to your skillset

Over 50 different courses are available through the program, covering technical skills, business acumen, problem-solving and machine learning. In the learning plan, Sreekanth has taken different modules customized to his skillset in a mix of instructor-led courses and training from peers on a different team. “You are asked about all the tools and your background,” he explains. “And based on that, they give you a customized learning plan.”

Sreekanth reflects on what he’s gained from the experience. “It’s not easy to capture real-time data and perform analysis in real-time. And there are some advanced tools like Kafka that Capital One uses, which I didn’t have experience with before. I was never exposed to that kind of work. But thanks to the training, I was able to interact with the team that is actually using it and understand from them directly how Capital One is using it.”


Ongoing learning for real customer situations

All of this learning comes in handy in Sreekanth’s role as a senior data engineer on one of Capital One’s Marketing and Analytics team. He works with Spark and builds Big Data pipelines on a data science team. The team has over 150 models, and an understanding of the full picture is key.

“All of these models need to be applied to huge data and be deployed in production through proper data governance principles. And for that, you need technical skills and principles to address security vulnerabilities and analytical skills to work with business teams,” Sreekanth said. “So that’s what I use day-to-day. As a data engineer, I provide support to the data science team. I make sure these models go through their production seamlessly.” It’s the data engineering team that brings these models into production for the end-users.

“Capital One offers great tools for you to learn because the company uses the most advanced tools available,” he shares. “As an engineer, you have a great opportunity to learn the latest tools. The management is supportive and they offer a lot of training for associates, so that’s how engineers grow here.”


To read more about Sreekanth’s journey at Capital One, click here.

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