Pros
Learning and Growth: Learning is my most important priority and Entelo provides that in spades! Every employee is given a $1K learning budget and I’ve used it for going to conferences as well as took many on-site classes to learn more about Machine Learning and Data Engineering. But it’s not just in the classroom where I learned, it was also in the projects I work on. When I started, I only knew Ruby on Rails, but during my time here, I did backend work that required learning about distributed systems, dove into hard problems with Elasticsearch, devops with AWS and technologies such as Docker and Kubernetes, and then Machine Learning and Data Engineering with Spark, Keras, and Tensorflow Engineering Culture of Collaboration and working with amazingly talented and trusting folks: 1. We don’t 100% pair, but whenever we get stuck or need a pair, any engineer would gladly spend the time to help one another 2. Although we’ve grown into product focused teams, we still actively cross communicate between teams to share ideas and work. E.g. React components that are shared across teams. 3. Data science, Product, and Engineering actively communicate together to solve problems quickly and make decisions in a timely manner. Unblocking is super important and that’s why our Daily Stand Ups include all 3 groups. 4. Engineering Happiness is how we score and prioritize technical debt Interestingly hard data problems: We have to index many different sites and integrate with many different data partners to provide our customers with the best experience possible. This means taking a very wide variety of structured and unstructured data and processing that showing that in a presentable way. Also, that data is then used for Machine Learning for a variety of predictions.
Cons
Growing pains: Making the transition from a small team and a small company where you knew everyone’s name to larger teams has been a difficult transition to me. I have to adjust to the extra effort required communicate across more people. It’s just more cognitive overhead than prior times. Code base that needs work: Lots of tech debt after years of growth. We have a cross team group that is focused on tech debt and quality so that’s a start.