Data Engineering Manager

We're looking for an experienced Data Engineering Manager to join the data team at Zapier. Are you interested in helping grow a product that helps the world automate their work so they can get back to doing work they love? Then read on…

We know applying for and taking on a new job at any company requires a leap of faith. We want you to feel comfortable and excited to apply at Zapier. To help share a bit more about life at Zapier, here are a few resources in addition to the job description that can give you an inside look at what life is like at Zapier. We hope you'll take the leap and apply.

Zapier is proud to be an equal opportunity workplace dedicated to pursuing and hiring a diverse workforce.

ABOUT YOU:

You are an effective team builder. This isn't your first leadership role. You know how to hire, train, and develop engineers from all backgrounds. You understand the benefits of building a diverse and inclusive data engineering team and leading them in an equitable way. You may not have hired or managed hundreds of people before, but you have hired, trained, and managed at least a few employees before.
You are a skilled written communicator. Zapier is a 100% remote team and writing is our primary means of communication.
You have experience managing technical data teams: you have kept things focused on execution, data correctness, and data timeliness on top of a constantly evolving data stack.
You have experience managing stakeholders. You’ve collaborated with stakeholders to understand and prioritize their needs. You advocate for your team and their needs by building strong relationships with engineering architecture, security and infrastructure teams.
You can communicate about technical topics without unnecessary jargon. You translate unfamiliar machine learning and data governance concepts into approachable ones for teammates with less experience working with data.
You understand that the perfect is the enemy of the good and default to action by shipping MVP and iterating as needed to get towards better solutions.
You have experience with at least one of the following:
Building Data pipelines: You’re familiar with ingesting and transforming data.
Dimensional Modeling: You’re familiar with Star schema in Data Warehouse, and can speak to the benefits of using them.
Infrastructure Operations: You are familiar with running technical infrastructure, preferably in Cloud settings.

THINGS YOU MIGHT DO

Zapier is a growing company, so you'll likely get experience on many different projects across the organization. Here are some things you'll get a taste of:

Develop effective ways to communicate, monitor, and lead your team through weekly one-on-one’s and team meetings.
Work closely with Data Engineering pods, Decision Science and product teams to create teams that deliver technical solutions and tools to our internal customers.
Help set technical roadmaps that will help Data Engineering achieve their goals.
Keep Data Operations management informed on your team’s progress in one-on-ones, update posts, and regular team hangouts.
Participate in code reviews, learning and spreading technical knowledge throughout all of engineering, data and otherwise, moving knowledge to documentation where appropriate.
Actively recruit, onboard, and train new engineers at Zapier with a focus on building diverse teams. This might involve tweaking the skills portions of interviews or writing better documentation.
Ensure the members of Data Ops are pulling in the same direction while also making sure we’re leveraging their diverse skillsets
As a part of Zapier's all-hands philosophy, help customers via support to ensure they have the best experience possible.

ABOUT DATA AT ZAPIER

Zapier relies on dozens of systems that emit data about Zapier and our potential and current users and partners. This data is useful for us to make a better product, better decisions, and understand our weaknesses and opportunities. The data team at Zapier pulls this data from DBs, APIs, and event streams, collocates it and then processes it through all the disparate systems to bring them together in a reliable, timely, performant, and easy to understand way to employees and systems that need it.

Within the data operations team, we're made up of several sub-teams: Data Engineering focusing on data infrastructure and storage, compute, ingest, and dimensional modeling; Data Products focusing on building statistical and ML tools and models; and Data Governance focusing on increasing the value of the data through stewardship. We work closely with our partner org, Decision Science who in turn focus on exposing the data in useful and self-service form and extract insights and recommend strategies based on their findings.

Our stack is best summed up by: AWS Redshift (and the related AWS products AWS Glue, Redshift Spectrum, AWS S3), Looker, Airflow, Matillion ETL, Kafka, Python, and NiFi. But we're pragmatic -- for example, we have some Java for ingesting data from Kafka, and we use Clojure for inferring schema and other information about data sets.

How To Apply

We have a non-standard application process. To jump-start the process we ask a few questions we normally would ask at the start of an interview. This helps speed up the process and lets us get to know you a bit better right out of the gate. Please make sure to answer each question.

After you apply, you are going to hear back from us, even if we don't seem like a good fit. In fact, throughout the process, we strive to make sure you never go more than seven days without hearing from us.