Humans of Atlassian: Data Engineering
Meet some of our Atlassian Data Engineers
This month, we met with a few representatives of the Data Engineering (DE) team at Atlassian. In a true spirit of #teamanywhere, we met async, as well as in virtual meetings and HHH (Half) Happy Hours. While it’s always a pleasure to catch up with fellow Atlassians, we decided to raise the bar and have a constructive dialog on each interviewee’s professional background, their current goals, and future career aspirations.
What path led you to data engineering?
Kriti: It all happened so organically! Back in 2010, I was working at MarkMonitor on automating the detection of counterfeit branded products. We used computer vision algorithms and were constantly running into roadblocks where petabytes of crawled data from search engines could not be processed by conventional RDMBS (relational database management systems). Eventually, we ended up setting our own Hadoop clusters and my journey with big data and distributed computing began.
Sneha: In my undergrad days, I was fascinated by using data to solve business problems. I was fortunate to get an opportunity as a fresh grad to work on building Data Pipelines, Analytical cubes (OLAP solutions), and Dashboards. This was the team’s first official data warehousing project, which served as the foundation for the organization’s and my data journey.
René: When I worked for Mayor Pete Buttigieg in the City of South Bend, IN — we tried to answer questions like “How many trash bins are we billing for vs how many trash bins are we picking up each week?” Getting answers took years as none of the data gathering/aggregation processes were automated. After three years of software implementations and process improvement we finally got good enough data and were able to drive big changes across the city. When I could run queries on good data to solve real user problems, I knew I was hooked.
Vivek: Since illustration and storytelling are my hobbies, business intelligence tools have fascinated me very early in my career due to their data visualization capabilities. In addition, they helped me to get closer to the business metrics and understand the definition of success. While getting deeper into building end-to-end BI solutions supported by accurate, reliable, and relevant data, I got into the world of data warehousing and data engineering.
During our conversations, everyone was passionate about the Atlassian values and real impact they create. People feel cared for, and it shows in every interaction — from the co-founder-led town halls to routine engineering standups. In addition, there are numerous projects each team works on cross-functionally, getting to know fellow Atlassians. This, of course, was an excellent segue into our next segment.
What is the most interesting project you have been involved with, at or outside of Atlassian?
René: Trash. I know, sounds odd, but picking up trash efficiently and effectively for a city has many steps. It’s also something very important.
Vivek: One of the exciting projects at Atlassian was taking over the ownership of the Tableau platform. This was quite a challenge for a team of data engineers with no prior Tableau experience. It was only possible due to the entire team living and breathing all five of the Atlassian values.
Sneha: Being a part of the Atlassian Cloud Migrations journey. Being a cloud-first company will help track, report, and forecast migrations for Atlassian internally, as well as for our customers.
Kriti: Enabling marketing teams to do omni-channel personalization and targeting by providing the data needed. This also gave me an opportunity to work and interact very closely with a wide variety of very skilled people at Atlassian and to learn from them.
Working in Data Engineering is similar to SDLC (software development lifecycle) patterns: even if it’s very quiet one day, there is no guarantee such “peace” will continue. Aside from our planned work (diligently documented each quarter and presented to leadership), there are ad-hoc requests to KTLO (keep the lights on), as well as data fix needs, (un)planned maintenance, and other deviations from the plan, considered BAU (business as usual). To stay organized and manage our workloads effectively, we asked each interviewee for some tips.
What advice do you have for people getting into data engineering?
Kriti: Care for your data! In its purest form, it is worth gold. With impurity, it is worth nothing.
Vivek: Today, data consumers are more diverse than ever and this technological diversity can become a struggle if we just focus on technology to achieve success. Hence, to deliver a successful data solution, we need to understand how the data is consumed. Is it helping to make better decisions, solve problems, improve operations and performance, or support key strategic initiatives?
René: Don’t lose the forest for the trees. We can get pretty specialized (data domain and specific tools), but we must always ask ourselves, “Is this really going to help the business answer the questions they should be answering?”
Sneha: Don’t get overwhelmed by the number of tools out there. Get your hands dirty, start by exploring unfamiliar projects, ask for help when needed and know your data.
In true Atlassian fashion to promote transparency and spread the word, we hope this article brought more clarity and understanding of the Data Engineering world.
Interested in a Data Engineering career at Atlassian? Check out our Careers page, sign up for the WorkLife blog, and stay in touch on social!
- Content edited for clarity and consistency.
