Bhagya Reddy is principal data engineer at QuantumBlack, a McKinsey company. At the Women in Data conference in November, she was named as one of the new list of female industry role models, the 20 in Data and Tech. She told DataIQ what it means to her and shared her views on the sector.
DataIQ (DIQ): Firstly, congratulations on being selected for the 20 in Data and Tech list. What do you feel about being included this year?
Bhagya Reddy (BR): I am extremely honoured to be receiving such an important award. I feel I am recognised well for what I have been doing for many years. Winning this award gives me good inspiration to work more and achieve more, especially in data sector, and to become a good role model for others.
DIQ: You are now viewed as a role model - is that a status you inhabit easily and were already active around (for example through mentoring or similar activities)?
BR: Due to the award, many more people are reaching out to me for mentoring than earlier and I am feeling more responsible and happy to mentor more inspirational women (or men) and to share my knowledge.
DIQ: How important do you see initiatives like Women in Data to be? Are there still gender and diversity issues to resolve within the data and tech sectors?
BR: I definitely think these initiatives are making a change and giving a platform to someone like me to inspire more women and to do more social activities and build awareness. I feel awareness of diversity issues is high now compared to a couple of years back - if we do these kind of inspirational events and continue to build that awareness, it could address the diversity issues soon in the data and tech sectors.
DIQ: Data engineering is a recruitment hot spot (high demand, low supply) - what view do you have of the need for these skills across your client base?
BR: I totally agree. Data engineering is in high demand, but there are few resources available in the market. Often, most of the people do data science-related work and women - or especially girls - feel engineering is a man’s job and we should bring awareness around this.
I feel if one understands the concept of data architecture, cloud computing and are good at programming language, as well as being able to find data insights, these are the key factors for becoming a data engineer.
DIQ: What impact has data science had on the demand for data and how it needs to be managed to support this function?
BR: Demand for data science in the last few years has increased 20-times more than earlier. Everyone sees that now data is the key factor for every business success or failure, ongoing business development or even for social media as well.
Data science models will be successful only if the right data is ingested into the model as well as being maintainable if the models run in production. Hence, I feel there is a huge need for having good data engineers and machine learning engineers, including QA (test engineers), as part of the team as a key to success for any production models.
DIQ: Is technology making the task of data engineering easier or more challenging?
BR: I feel technologies are making the task of DE easier if we choose right platform and technology as per the needs.But lots of technologies are in market, which might confuse the DEs if they are not considering the future needs of the project and the end goal of the problem what they are working on.