Patreon is a media and community platform where creators give their biggest fans access to exclusive work and experiences. Over 300k creators are cultivating fandoms and building their businesses each month. Creators can offer free memberships to fans looking to explore more of their work, paid memberships to give access to exclusive media and community, or sell directly to fans with Shops.
Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with:
8B+ earned since Patreon's inception
30M free new memberships in the first year of launching that option, and
10M fans paying each month for exclusive access to creators' work and community.
We're continuing to invest heavily in building the best creator products with the best team in the creator economy and are looking for a Data Engineer to support our mission.
This role is available to those wishing to work in our San Francisco and New York offices on a hybrid work model or those wishing to be fully remote in the United States.
About the Role
Work on a tight-knit team of highly motivated and experienced data engineers with frequent collaboration with data scientists, product managers, product engineers, finance and operational teams.
Build core data sets and metrics to power analytics, reports and experimentation.
Write real-time and batch data pipelines to support a wide range of projects and features including our creator-facing analytics product, executive reporting, FP&A, marketing initiatives, model training, data science analytics, A/B testing, etc.
Advocate for best patterns and practices in areas of data pipeline design, performance and testing of datasets.
Be a driver of a data-centric culture at Patreon. Work autonomously on large green field initiatives and help define data best practices at the company.
About You
Expert in SQL, Spark and Python or Scala
Significant experience modeling data and developing core data sets and metrics to support analytics, reports and experimentation. Solid understanding of how to use data to inform the product roadmap.
Enjoy collaborating with Data Scientists, Product Managers, Product Engineers, Financial and Operational teams. Comfortable playing the role of a Project Manager in order to drive results.
Have previously built real-time and batch data pipelines using tooling such as Airflow, Spark, Kafka, S3, Fivetran, Census, etc. Experience working with event tracking frameworks, data observability frameworks and experimentation frameworks.
Solve our most challenging data pipeline problems, utilizing optimal ETL patterns, frameworks, query techniques, sourcing from structured and unstructured data sources.
Experience working with Data Platform Infra, including Data Warehouses and Data Lakes such as Databricks, Redshift, Big Query, Snowflake, Delta Lake, etc.
Highly motivated self-starter that is keen to make an impact and is unafraid of tackling large, complicated problems and putting in the work to ensure high craft deliverables.
About Patreon
Patreon powers creators to do what they love and get paid by the people who love what they do. Our team is passionate about making this mission and our core values come to life every day in our work. Through this work, our Patronauts:
Put Creators First | They’re the reason we’re here. When creators win, we win.
Build with Craft | We sign our name to every deliverable, just like the creators we serve.
Make it Happen | We don’t quit. We learn and deliver.
Win Together | We grow as individuals. We win as a team.
Patreon is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class.
Patreon offers a competitive benefits package including and not limited to salary, equity plans, healthcare, unlimited paid time off, company holidays and recharge days, commuter benefits, lifestyle stipends, learning and development stipends, patronage, parental leave, and 401k plan with matching.
