Why choose to be a data engineer in a consulting firm? What are you doing at McKinsey? Are you a consultant or engineer? My tech-geek friends had these questions when I joined McKinsey.
At the firm I am an engineer and a consultant, which makes the job fun and challenging. I work with team members to define and break down complex business problems and leverage my expertise in data engineering to solve some of the toughest client challenges. That’s why I enjoy my work.
An “only at McKinsey” project
My most memorable project was one where we worked with the firm’s technologists, industry experts and consultants to deliver for a mining company in North America. We leveraged machine learning and Cloud technology to improve the performance of a smelter plant in a remote area.
My team worked closely with one of the firm’s industry specialists and we found that the legacy report on the client’s server didn’t capture all the relevant information. Additionally, the previous analyses could not run at a large scale or in real time because of power limitations. Even under the best setup, some equipment may still not work well. To solve the problem, we worked with one of our Internet of Things (IoT) experts and looked into sensor signals and data to understand where we could obtain fresh data and how we could best use it.
My team and my role
As a data engineer, I worked closely with a data scientist, software developer, and DevOps practitioner to integrate thousands of IoT sensors, financial data, chemical information from the lab, text from the maintenance log and work orders into a Cloud data lake. This enabled us to run complex analyses and machine learning models to optimize control parameters, predict equipment breakdown, and monitor device conditions and working efficiency.
Not your average data engineering experience
This experience was very different from those I had working in industry, where I would sit in an office and code a data pipeline. Being at the client site, plugging in data pipelines to influence the metal processing pipeline was completely different. And, our solution could not have been achieved without the power of our team and all its members.
We completed this North American metals refinery project successfully with an impactful outcome—our lead-recovery optimization helped the client sustainably reduce the by-product (slag) in its lead production, with projected gains of $4-6 million per year; increased metal recovery helped the slag fuming furnace operation deliver $4 million; and increased recoverable metals from our feed-blend optimization delivered an annual impact of approximately $6.3 million.
Variety is the key
Fast forward a few months, I returned to Tokyo and began a new project for a large retail company, dealing with data from hundreds of stores. From one project to the next, I repeat my role as engineer and consultant but never repeat the type of problem I am solving. With each project, I meet remarkable, talented people with diverse backgrounds who continually teach me new things. I am never bored and I grow every day.
More about Xilong
Xilong is a principal in data engineering with QuantumBlack in Tokyo. He holds a masters degree in information systems development, with a focus on data modeling and data development from Han University in the Netherlands. Prior to McKinsey, Xilong was a data engineer at Active Network and a consultant at QUBIX and PwC Advisory.
Outside of work, Xilong enjoys traveling (pre-COVID-19), hiking, and skiing.