We're looking for people with a strong background in data engineering to guide the vision for data schemas, tooling, and pipelines at Stripe. The team's work will provide Stripe with visibility into how products are being used and how we can better serve our customers.
- Grow a team of data engineers to continue to scale our data pipelines, drive the collection of new data and the refinement of existing data sources, and improve our data model and instrumentation as the Stripe product evolves
- Build relationships with engineering teams, product managers, and data scientists to generalize data needs and guide the roadmaps of our internal platforms
- Mentor and grow your team across technical architecture, partnership with stakeholders, project management, and a strong internal product sense
- Help to build and scale a production experimentation service as well as an analytical experiment platform
- Help identify and build shared libraries and resources for Data Science work such as, forecasting tooling, anomaly detection at scale, and support for different machine learning approaches
We're looking for someone who has:
- 6+ years of experience in a Data Engineering and/or Data Science role, with a focus on building data pipelines or conducting data intensive analysis.
- 3+ years managing and building a data engineering team.
- Expertise with Scala, Python, and SQL.
- Experience writing and debugging data pipelines using a distributed data framework (Hadoop/MapReduce/Spark/Pig etc…)
- An inquisitive nature in diving into data inconsistencies to pinpoint issues
- The ability to communicate cross-functionally, derive requirements and architect shared datasets
Some things you might work on:
- Develop unified user data schemas and tables that provide a complete view of the business across our various products such as Stripe Connect, Atlas, or Sigma
- Build data pipelines that track our marketing funnel from visits to onboarding to active usage of Stripe
- Work on our centralized experimentation platform to pipeline experiment metrics and compute descriptive statistics