Job Summary
Pipe is a new kind of trading platform that enables entrepreneurs to grow their businesses on their terms. By treating recurring revenue streams as an asset, Pipe allows companies to transform their recurring revenue into up-front capital, instantly. For entrepreneurs, that means more cash flow for scaling a business without dilution or restrictive debt. For investors, Pipe has unlocked a previously untapped asset class. Whether you’re an entrepreneur or an investor, Pipe is growth on your terms. We’re a fully distributed, remote-first, fast-growing startup. Our engineering and data teams are spread from UTC-8 to UTC+6 and we rely heavily on our written communication skills in order to make it work. We believe in giving our team agency and control over their schedules: we avoid standing meetings, and default to asynchronous communication. There are no core working hours, we just ask our team to communicate clearly about their schedules and be considerate to their coworkers if plans change. You will occasionally need to be flexible in order to meet synchronously with colleagues in different time zones.
- Minimum Qualification:Degree
- Experience Level:Mid level
- Experience Length:3 years
Job Description/Requirements
- Build data systems and pipelines to enable high-velocity model development, and self-serve analytics and reporting for teams across the company.
- Own and improve various data infrastructure, including the data warehouse, distributed compute clusters, and model deployment tools.
- Build tooling, testing and processes to drive data reliability, integrity and availability for applications across the company.
- Help define flexible and scalable schemas across the data stack.
- Independently and proactively find opportunities in our data model to unlock business value.
- Ensure data safety, security, and regulatory compliance.
Qualifications:
We are looking for talented data engineers with past experience in a similar role. Ideal candidates will have:
- Experience building ELT/ETL pipelines
- Expert-level proficiency in SQL and at least one programming language
- Experience with distributed computing frameworks such as Spark
- Familiarity with building model deployment tooling and model hosting infrastructure
- Bachelor's degree in Computer Science, or another technical field, or equivalent working experience
- Strong written and verbal communication skills
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