Who we are
Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.
About the team
Stripe is the best software platform for running an internet business. We handle billions of dollars every year for hundreds of thousands of businesses around the world. One third of Americans bought something on Stripe in the last year.
With all this data, the Growth Data Engineering team is looking for talented data-minded engineers to help us manage business critical data leveraged across the entire organization. If you are passionate about data, excited about designing data pipelines and data-driven user experiences, and motivated by having an outsized impact on the business, we want to hear from you.
What you’ll do
Every record in our data warehouse is vitally important for the businesses that use Stripe, so we’re looking for people with a strong background in data engineering and analytics to help us scale while maintaining correct and complete data. You’ll be working with a variety of internal teams across Growth, Sales, Marketing, and Data Science to help them solve their data needs. Your work will provide teams with visibility into how Stripe’s Growth organization is performing and how we can deliver a better experience to Stripe's customers.
- Identify data needs for Growth, Sales, and Marketing teams, understand their specific operational and reporting requirements, and build efficient and scalable data products & pipelines to enable data-driven decisions across Stripe
- Design, develop, and own data pipelines, models, and products that power Stripe’s Growth, Sales, and Marketing teams
- Help the Data Science team apply and generalize statistical and econometric models on large datasets to empower more intelligent decision making among our Growth & Go-to-Market teams
- Develop strong subject matter expertise and manage the SLAs for both data pipelines and full stack web applications that support the Growth & Go-to-Market organizations at Stripe
- Build Stripe's Customer Engagement Data Service - collecting, curating, mastering, enriching, and presenting a single view of user interactions with Stripe for consumption by data applications across the company
- Build and refine Stripe's data foundations - infrastructure, pipelines, and tools to enable Growth, Sales, and Marketing teams at Stripe - working with Scala, Spark, and Airflow
- Design and build client libraries and frameworks to log events and accurately track the behavior of users interacting with our logged-out user interfaces such as Stripe.com
- Refine our existing data marts that help the Sales and Marketing organization at Stripe forecast the future potential performance of the business, and reliably measure their ongoing attainment toward targets
- Build data pipelines that track key GTM product metrics, and measure the impact of different GTM strategies employed by teams in the field
- Integrate different parts of our experimentation infrastructure at Stripe, to enable full-funnel measurement and personalization of experiences spanning from Stripe.com into product
- Our stack spans tools in Spark, Scala, Python, Spark, SQL, Presto, Airflow, AWS, Java, Go, and React
Who you are
We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
- 3+ years of experience in a Data Engineering or Software Engineering role, with a focus on building data pipelines, or applications powered by big data.
- A strong engineering background and are interested in data
- Prior experience with writing and debugging data pipelines using a distributed data framework (Spark / Hadoop / Pig etc)
- An inquisitive nature in diving into data inconsistencies to pinpoint issues, and resolve deep rooted data quality issues
- Knowledge of a scientific computing language (such as Scala or Python) and SQL
- Experience with full stack development languages such as Java or Go, and front-end frameworks such as React
- The ability to communicate cross-functionally, derive requirements and architect shared datasets