What do the career path and the day-in-the-life look like for a Principal Data Engineer at Recorded Future?
We connected with Lilian Cheung to find out!
Interested in learning more about Recorded Future and their job openings? Make sure to check out their company page on VentureFizz!
Where did you go to college? What did you study and what were some of your initial jobs out of school?
I graduated with a B.S. in mathematics-statistics and economics at the University of Connecticut, then earned a Master's in statistics from North Carolina State University.
Throughout school, I worked on a number of statistical consulting projects. I enjoyed learning about my clients' research projects and picking up new statistical software. My first job outside college was at a startup as part of a small, lean team building fast-paced projects. I loved the startup environment!
What has contributed to your success and propelled you to the position you have now?
I followed a nonlinear path to reach where I am today. At one point, I wanted to be a teacher. At another, I considered pursuing a career in research and academia. I took time after college to section hike the Appalachian Trail. Hiking the AT, where I embraced the challenge of testing my limits, was a pivotal moment for me.
90% of what I do involves skills I learned on the job. When I joined Recorded Future, my motivations were to (1) build something that matters, and (2) keep learning. I started as a data engineer, then became a tech lead and senior data scientist. My technical interests and job responsibilities span across data science and data engineering. Every role and every project presents a set of challenges and an opportunity to learn new skills. Each day, I build tools that keep people safe from cyberattacks. Given the world we live in today, cybersecurity is important now more than ever.
Can you share the high-level responsibilities of your current position as a Principal Data Engineer at Recorded Future?
I am an individual contributor and one of the leads on the Data Curation and Quality team. As an individual contributor, my role involves building data pipelines, assessing data quality, and shipping production code. As a lead, my responsibilities include weighing in on technical questions/processes, unblocking team members, and driving technical discussions to ensure each member of the team is well-prepared to access various parts of our internal systems.
What professional accomplishment are you proudest of?
Launching Identity Intelligence: I was one of a handful of engineers on the Data Science team working to launch the Identity module. As a team, we wrote pipelines to detect leaked credentials and warn clients about data stolen by malware. We needed to move quickly and accurately to prioritize the features most important to clients. The project challenged me to communicate designs, pull together with my teammates, and deliver a new product line under tight deadlines.
Any tips for someone considering a career in your field?
Keep learning. Don't be afraid to find your own way: Hike your own hike. These principles have served me well:
- Do your best every day.
- Be willing to disagree and commit. Voicing opinions is critical to getting the perspectives needed to reach an optimal solution. Once the team agrees on the solution, execute it to the best of your ability.
- Learn from your teammates. Every one of my coworkers offers unique strengths and approaches to solving a problem. My team pushes me to be a better engineer.
- Have a plan for how to tackle challenges. Driving consensus is much easier with a plan.
Day in the Life
Coffee, tea, or nothing?
One cup of instant black coffee per day.
Every day is different, but can you outline what a typical day looks like for you?
This is my schedule on a fairly typical Tuesday:
Between 8:30 am - 9:00 am: Log in. Check Slack messages, emails, tickets, and/or alerts. Set goals for the day.
9:15 am - 9:30 am: Daily status update in the team standup.
9:30 am - 9:45 am: Status update in a cross-team project standup. There are a couple new requests for mock data from our team. We've already completed the data pipelines; the mock data is one of the last requirements to enable UI tests.
10:00 am - 11:00 am: Team refinement session. The team has 1-2 weekly refinement sessions to discuss ticket requirements and estimate how much time tickets will take to complete.
11:30 am - 12:00 pm: Meeting with a couple coworkers on a project we're working on. We discuss the remaining work and start reviewing related code from earlier in the pipeline.
12:00 pm - 4:15 pm: Time to code! I tackle the creation/uploading of data discussed earlier to unblock other teams working on the same project.
4:15 pm - 5:00 pm: Data Science team Fika (informal coffee/study break): One of my coworkers introduces an internal tool.
Between 5:00 pm - 5:30 pm: Wrap up tasks and log off for the day.
Any productivity hacks?
Reminders: I use Google Calendar reminders to plan tasks and keep myself on track.
Documentation: Documenting solutions saves so much time when similar problems pop up again.
Building a personal knowledge base: I use Notion and Obsidian to organize useful snippets of information. I regularly refer to these notes when solving technical problems.
