Ever wonder what it’s like to work on our Technology Team? We sat down with a few members of the team to find out more!
What’s a day in the life like on your team?
As a member of the Tech and Analytics team, we start each day with a meeting to get to know what everyone is working on and make sure no one needs help with an issue. If that requires any follow-ups, we make sure those get scheduled or talked through immediately.
As part of the Analytics team, most days include working on reporting for our programs – either kicking off data pulls or working through editing the data pulled. If there are any issues there, we work closely with the Sales team to determine a path forward.
We often receive requests or find the need for new metrics or improvements on the current reporting and spend time developing reusable code to provide those results. We use GitHub to keep our code organized and ensure everything is peer reviewed and statistically sound before using the code to answer requests.
Outside of those daily tasks, weekly or biweekly meetings are setup to discuss and plan future improvements or additions to our reporting, documentation, quarterly goals, personal goals, and sprint goals. We also like to keep things fun and keep connected through Slack and all the fun social channels available!
-Rachel, Data Analyst
What does the Technology team do for fun?
For fun, the Technology team jokes with each other, gets together for off hours gatherings (when available) and build friendships within the team. We take interest in each other’s personal lives and hobbies to grow as a cohesive group. End of Sprint Trivias, Morning jokes and finding the humor in any bad situation keep us laughing and smiling throughout the day.
-Howard, Scrum Master
What advice do you have for someone who may be starting in your function?
I can’t stress enough how important it is for a data engineer to have a strong programming background. It also needs a love of or at least an interest in data, in finding patterns in data. Also, as a programmer, you must like and can create systems that are difficult and complex. So it’s a love of data combined with a love of programming to create data pipelines. A data engineer has three main duties:
- To ensure that the data pipeline – the acquisition and processing of data – is working
- To serve the needs of internal customers – the data scientists and data analysts
- To control the cost of moving and storing data
“The critical skills are SQL, Python, and R, and ETL methodologies and practices.” Apart from technical skills, a good data engineer should also have certain soft skills and qualities:
- Attention to detail: Data quality is extremely important when building pipelines. All downstream work is only as good as the quality and integrity of the data you’re moving through the pipeline. You must really care about and appreciate the “garbage in, garbage out” principle.
- Appreciation for clean design: There’s never one way to design and build a pipeline for moving data from point A to point B. A good data engineer should appreciate the elegance of clean and simple designs that are not over-architected.
- Good communication skills: A lot of times there’s a discovery period when you start to design a pipeline because your data is sitting in different silos that may be in different areas of your infrastructure. You’ll have to talk to people to understand the playing field before you design anything. This discovery step isn’t easy, but it’s a requirement for making sure you’re building the right thing. A good data engineer should find satisfaction in helping their customers solve painful problems.
- Excitement about working on back-end systems: Data engineers don’t build a lot of UIs and front-end apps. They work deep in the systems stack, and in many cases, they won’t be able to point to something shiny and say “I built that!” You have to be OK with that and take pride in being the hero behind the scenes.
- A love of learning: This isn’t really data engineering-specific, it’s just how the software engineering world operates. You have to keep up with new libraries, frameworks, and tools out there in the community. Things change fast and you need to be able to quickly understand, evaluate, and learn new tools if necessary.
-Bharath, Data Engineer
Thanks to the Technology team for providing a look behind the scenes!
