Numbers, networks and new possibilities: How Rachel Sitoh found her future in data engineering

When Data Science and Analytics alumnus Rachel Sitoh first joined global investment management firm Qube Research and Technologies as an intern, she did not expect to find herself immersed in the intricate world of data pipelines, market signals and alphas. What she discovered, however, was a role that brought her passion for data engineering to life - and set her on a clear career path.

“I realised very quickly that what I was learning aligned closely with my career goals. This was the kind of environment I wanted to grow in,” she says.

During her internship, Rachel gained a front-row view of how insights unlocked from data shape financial decision-making in a fast-paced environment. Working closely with the firm’s quantitative researchers, she applied her coding skills to organise and structure vast amounts of financial data, building datasets that would be used to generate trading signals, or alphas - which influence market performance.

“The tasks I worked on weren’t just theoretical exercises,” she adds. “They had a direct impact on profitability and it was rewarding to see my work contribute to that.”

Now a Data Engineer at the same company, Rachel continues to build on the very foundations she developed during her internship, applying her skills at greater scale and complexity - from onboarding new datasets to organising data to support deeper analysis and writing well-structured, adaptive codes.   

Looking back, Rachel sees her internship as the cornerstone that paved the way to a smoother start in her full-time role. It was where she learned the realities of working with data at scale - how to manage massive datasets efficiently, ensure data accuracy and consistency and build systems robust enough to run with minimal oversight. “One of the biggest insights I took away was how crucial it is to keep data clean, accurate and accessible,” she says. “In this field, being both fast and precise makes all the difference.”

Beyond technical skills, the experience also taught her that the true power of data science isn’t just in coding or crunching numbers - it depends as much on collaboration and communication. Today, Rachel focuses on commodity fundamentals, futures and foreign exchange data - but emphasises that her work is not purely technical.

“Data only becomes meaningful when it meets the needs of the people who use it,” she explains. By working closely with researchers from different teams, she is able to better structure datasets in ways that make sense for them, occasionally combining data sets with different coverage so that researchers have a more complete data reference source.

While her internship focused on learning and support, her current responsibilities extend to thinking strategically about the value and impact of her work. “I now think more about how my work fits into the bigger picture,” she says.

Those lessons have stayed with her as she takes on greater responsibilities as a full-time data engineer. “I realised how much I enjoy the technical aspects - the building and optimisation - and how much there still is to learn,” she says.

Now, Rachel hopes to deepen her expertise and develop a niche in specific financial products. “Working with a wide range of data showed me just how many possibilities there are in this field.”