I graduated from Nanyang Technological University in 2017 with a bachelor’s in engineering, information engineering, and media. Prior to McKinsey, I worked as a management associate for Singtel, and as a data engineer at Lazada Group. During that time, I earned my master’s degree in computing specializing in artificial intelligence from the National University of Singapore.
I thought my options were limited to Internet companies but I was passionate about data and wanted to work with clients. In July 2019, I read that McKinsey had launched QuantumBlack in Singapore and made up my mind to join the firm.
Fast forward to December 2020, I accepted a position as a data engineer on the Singapore QuantumBlack team. This role fuels my passions for data and advanced analytics and for helping clients solve their biggest challenges.
McKinsey’s mission resonated with me
At one point I was interviewing with four companies across hot industries, such as cryptocurrency, ecommerce, gaming, and social media. I, however, was drawn to McKinsey’s dual mission “To help clients make distinctive, lasting, and substantial improvements in their performance and to build a great firm that attracts, develops, excites, and retains exceptional people.”
My personal goals align with the firm’s values and purpose. Plus, generally speaking, tech firms have a much better starting point when it comes to data and analytics, compared to many of McKinsey’s clients that existed well before the current data-centric era. It’s rewarding to be part of our clients’ transformations.
Building assets to solve business-critical problems
I use data and advanced analytics to build strategies which address clients’ pain points. I work closely with data scientists, business analysts, and clients’ data analytics teams.
My favorite project so far is a product that could serve clients across most of our industries, especially those at the early stage of their “data on cloud” journey. It is a data ecosystem accelerator which enables organizations to rapidly stand up and scale core data capabilities required to develop and operate prioritized data-driven use cases (i.e., data products, reporting, advanced analytics and exploration) in an agile manner.
Basically, when many services are involved, manually configuring and managing each of them can be tedious and time-consuming. This solution will help clients adopt Infrastructure as Code, so we will spend less time setting up and managing cloud infrastructure. To help clients be more successful, we embed best practices into the product, such as a codified data quality framework used by both batch and real-time data processing. We use Terraform to achieve Infrastructure as Code and Python to build process acceleration modules, such as data quality framework and data sources connectors.
It’s been such a learning experience, and honestly, I never expected to do product development in a consulting firm. And, I never expected a consulting firm to be like McKinsey... it’s a much different and more diverse place than I thought it would be.
Thriving in a learning culture
On top of the substantial learning materials and workshops, I have access to the best talent in the data industry. In my short time here, I’ve heard leaders say, “It is my responsibility to help you succeed,” and their actions back up those words. I would not have the confidence to do what I do without their encouraging words and selfless guidance.
More about Jessie
Jessie follows the Data Engineering Guild and Women Who Cloud groups at McKinsey and has taken advantage of McKinsey’s vast learning opportunities. Inspired by her first project, she recently passed the AWS certified cloud solution architect associate exam.