Team Nextdoor

Meet the Interns: Part 1

Written by ndmulti

This summer, we had several interns hard at work across different teams, ranging from engineering to data to design.

Check out what they’ve been working on and hear about some of their fondest memories and technical (and non-technical) lessons learned from Nextdoor.

Adlai Gordon

Studying: Computer Science @ Boston University 2016
Special Talent: Reciting Dr. Seuss’s The Lorax from memory
Mentor: Niall O’Higgins

Project: This summer I worked with the Growth team on a variety of projects, including building an automated data pipeline from Amazon S3 to our Redshift database. The data needed to be decompressed and error-checked first, but the files were large and would only continue to grow as Nextdoor does. Instead of downloading the entire dataset to memory, the data is decompressed on the fly as it is streamed to the database. In addition, the data update is fully atomic – it takes place in a transaction and is automatically rolled back if any errors are found.

What I Learned: Among a few different projects, I improved my skills in test driven development, data analysis, and API interaction. In addition to technical skills, however, my biggest enlightenments were learning how a company like Nextdoor functions. I bettered my understanding in Agile development, and saw how it was practically applied and tailored to fit particular teams. Observing quality executives and managers build team culture and delegate responsibility showed me how a company can move quickly, make users happy, and keep everyone engaged. This fall I will be president of the Boston University Outing Club, and I leave this internship far more confident to be part of and to lead an organization.

Favorite Memory: Refine week: For a week in July, normal operations of the company were put on hold to form small teams working to improve the site, internal tools, and more. I worked with a team of eight to understand why and when people deactivate and reactivate their Nextdoor accounts, and how we can improve our member experience to this regard. Even though I had only been an intern for a few weeks, I suddenly found myself the only engineer on the team! I was excited by the role, and worked hard to produce results as I was the only person implementing the changes that we spent each day designing.

Andrew Tran

Studying: Computer Science / East Asian Studies @ Princeton University 2016
Special Talent: Dialpad music
Mentor: Grant Zhu

Project: I work within our core Android team to build a sustainable and scalable architecture for current and future feature development. I’ve helped create internal tools such as an interface for developers to toggle the state of the app by flipping switches to control the reachability of certain code paths and am now preparing the client for realtime, synchronous messaging.

What I Learned: It’s an exciting time to be working on an application that’s beginning to tackle the challenges of innovating at web scale. On the core mobile team, we’re tasked with making decisions that affect the ongoing process of development on Android and as a result I’ve learned firsthand from the amazing people around me how to evaluate deep technical tradeoffs that carry consequences for real, consumer-facing ramifications.

Favorite Memory: Watching one of our senior engineers navigate a ropes course in pristinely shined dress shoes.

Cissy Chen

Studying: Computer Science / Applied Math @ Princeton University 2016
Special Talent: Read ekphrastic poem I wrote
Mentor: Wenbin Fang

Project: I created a Compose button to send private messages from the Inbox on our website. This button has been a highly requested feature for web so it’s been exciting to work on. The compose Bootstrap modal uses the Select2 plugin to allow users to select a recipient from their neighbor list (with search/auto-complete functionality). In preparation for launch, I created a Tableau dashboard to track how the button was impacting our private messages.I’m also learning some iOS development through adding an embedded browser view to allow neighborhood Leads to verify neighbors on the Nextdoor iOS app. This is a highly requested feature from our valued Leads and the embedded web view is a low-cost solution that will help us quickly deliver this functionality to them and also quickly iterate. This will also serve as an example of a native-feeling web view that can be used as first iterations of certain appropriate features.

What I Learned: As this is my first internship at a tech company, I learned that the lifecycle of a feature, from the idea to the launch, requires you to exercise many of your “soft” skills in addition to your coding ones. I coded, yes, but I also worked on product specs, wrote documentation/user-facing blog posts, and discussed product roadmaps with people. At a company like Nextdoor that’s building a product that impacts so many users, you really need to think and test carefully to make your code more than a hackathon project.

Favorite Memory: Being a part of the internal co-op responsible for getting this Engineering Blog set up. We’re a data-driven “startup” within another data-driven startup.

Danyal Rizvi

Studying: Computer Science / Math @ University of Texas 2016
Special Talent: Flipping toothpick in my mouth
Mentor: Shiv Ramamurthi

Project: I work on the TenX, or Triggered Engagement and Newsfeed Experience team; we’re responsible for all engagement related features. Everything I’ve done, and everything the team does, revolves around bringing the user back to Nextdoor. I worked on large scale analysis studying the distinct features that convert a casual Nextdoor user into what we call a Weekly Active User, built an interactive dashboard to visualize the results of a variety of A/B tests we run on a regular basis, and started working on a service that notifies our data team about failures with our ETL pipeline.

What I Learned: Through studying the distinction between casual users and active users I had the opportunity to become familiar with the ipython notebook environment, learn how to write optimized Postgres queries, and do a bit of simple statistical analysis. In building the A/B testing dashboard, I got to hone my skills using advanced features of Tableau and learned how to optimize Tableau dashboards for performance. Writing the service for ETL errors, I had the chance to interact with Sendgrid and Django.

Favorite Memory: My favorite memory from Nextdoor was actually at one of the many fun intern events we had through summer: the panic room, a game where you’re locked in a room with the backstory of being a time machine lab and have an hour to solve puzzles to lead you to a key. The moment was when we’d been minutes away from coming to the solution as the buzzer went off and Eric, our infrastructure intern, figured and yelled out the solution just as we ran out of time. Though we lost, we had a great time and laughed it all off.

Blanca Villanueva

Studying: Computer Science (Artificial Intelligence) @ Stanford University 2016
Special Talent: Taiko drumming with Stanford Taiko
Mentor: Vikas Kawadia

Project: I work on the Growth team at Nextdoor, where my primary focus is on postcards. A unique way in which Nextdoor draws in new members is through its postcard program, which allows members to invite neighbors onto the site by sending physical postcards at no cost to the sender. This summer I’ve conducted and contributed to analyses exploring, for example: the effects of message customisation and social sharing on sign-ups, and the UX-driven incentives that bring people to invite new neighbors to Nextdoor. Our analyses involve creating A/B tests as well as predictive models. My next project is to create a predictive model relating to postcard conversion rate.

What I Learned: Working with Nextdoor’s data pipeline has impacted how I think about systems architecture, and is fascinating from an intellectual standpoint! I hadn’t worked with such a complex dataset and pipeline before, and so getting the chance to tinker with Nextdoor’s codebase using a combination of in-house tools, PostgreSQL, Tableau, and other third-party services has been a great learning experience.

Favorite Memory: Seeing all new interns’ and hires’ special talents! They say “the bar is set very low”, but all of the ones I’ve seen so far have been great.

Levi Wolf

Studying: Geospatial Analysis & Computation, PhD @ Arizona State University 2018
Special Talent: Country/Bluegrass Music
Mentor: Jay Thomas

Project: I worked with our Business Intelligence and Geospatial team to build a pipeline to integrate US Census demographic data with Nextdoor data. To do this, I built cenpy, a package to explore and use US Census data products interactively and dynamically. In addition, I worked on space-time statistical modelling for forecasting and trend detection.

What I Learned: Coming from a PhD program and working in industry was both exciting and challenging. Learning the real-world demands on the statistical techniques and tools I’ve been building for my degree has strongly improved my understanding of how end users actually end up applying the models academics develop. In addition, I’ve learned a bit of how to bridge the gaps between the two communities.

Favorite Memory: The Town Hall meeting. It was amazing to see the company all come together as one community and collaborate openly and effectively on communal issues.

To be continued…tune in next week to meet the rest of our interns!

Check out more content from the Nextdoor Engineering Blog.

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