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's Optimized Checkout Suite (OCS) consists of: 1) an optimized payment surface, 2) dynamic payment methods, and 3) Link (allow customers to reuse payment methods for a faster checkout experience). These features help businesses maximize revenue, increase conversion, and save thousands of engineering hours. Businesses also saw a +11.9% average increase in revenue after migrating to Stripe’s Optimized Checkout Suite.
We’re not stopping there though, and have many more ways we’re working on providing a highly converting, revenue maximizing experience for merchants via our suite of checkout solutions. Data integrity and experimentation are critical to our success, and we are focused on building data pipelines to support this optimization work. Specifically we’re investing in increasing the accessibility and comprehensiveness of our datasets.
This work requires close collaboration with our engineering, product management, and data science teams, and this role includes opportunities to deeply understand complex product experiences and user behavior, as well as solving technical challenges at a large scale. If you are passionate about designing data pipelines and building data-driven user experiences, and are motivated by our goal to build an industry leading, world class support experience, we want to hear from you.
What you’ll do
You’ll help build and maintain fundamental building blocks for operational and support data insights. Stripe provides many products with a wide variety of usage patterns, creating a high degree of scale and complexity for users around the world. We’re looking for people with a strong background in data engineering and analytics to help us scale while maintaining correct and complete data.
Responsibilities
- Identify data needs for supporting conversion and revenue optimization efforts relating to Stripe’s Optimized Checkout Suite.
- Design, develop, and own efficient and scalable data products & pipelines to enable data-driven decisions across Stripe
- Help data science & ML teams apply and generalize statistical models on large datasets to empower more intelligent decision making
- Understand business questions and determine efficient data pipelines for answering those questions
- Build and refine Stripe's data foundations - infrastructure, pipelines, and tools to enable stakeholders working with Scala, Spark, and Airflow
- Design and build client libraries and frameworks to log events and accurately track important usage and behavior information
- Build data pipelines that track key operations & product support metrics to help measure the impact of different strategies employed by operations teams
- Integrate with experimentation infrastructure at Stripe, to enable full-funnel measurement and personalization of experiences for buyers (end users of checkout surfaces)
- Help influence, create, and maintain best practices and data standards for tooling, querying, and reporting (including correctness, consistency, privacy, and timeliness)
- Our tech stack primarily spans Spark, Scala, Python, SQL, Presto, Airflow, MongoDB, AWS, Java, Go, Ruby, 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.
Minimum requirements
- 2+ 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 high interest in data
- Prior experience writing and debugging data pipelines using a distributed data framework (Spark, Hadoop, Pig, etc.)
- An inquisitive nature for diving into data inconsistencies to pinpoint issues, and resolve data quality issues regardless of the cause
- Proficiency with a scientific computing language (such as Scala or Python) and SQL
- Experience with full stack development languages such as Ruby, Java, or Go, and front-end frameworks such as React
- The ability to clearly and effectively communicate cross-functionally, derive requirements and architect shared datasets
- A passion for supporting both external users and internal customers
- Strong product sense and appreciation for different user behavior and usage patterns
- Have a high quality bar, attention to detail, and you help your team deliver polished products
Preferred requirements
- Ability to thrive with a high level of autonomy and responsibility
- Familiarity with Artificial Intelligence and/or solving problems using Machine Learning optimization strategies.
- Experience developing data intensive applications
- Experience leading a team of or mentoring other data and software engineers
- Experience developing, participating, or influencing team or company data strategy
- Experience and familiarity with any or all of the technologies mentioned above
- Strong growth mindset with a desire to learn by tackling challenging problems
