Paperchain is an early-stage startup building real-time revenue forecasting and financial tools for the digital media industry using unique applications of data science and blockchain technology.
We’re looking for a Data Engineer to come in and help build our data pipeline foundations at Paperchain. This is a contractor role with the plan to transition into a full-time role. The core team functions remotely with HQ being in New York and our CTO in Berlin.
College education is not a prerequisite. If you did go to college, we place no weighting on where you went. We encourage women, non-cisgender and people from non-white European ancestries to apply.
In this role, you will be responsible for working with the CTO on data strategy and building data products at Paperchain. Your work will have a direct impact on our customer onboarding journey and will also help influence the strategic decisions at the company.
As a data engineer at Paperchain, you will:
- Help build and maintain the ETL data pipelines from external sources like Apple, Spotify, YouTube, Google into our data warehouse (BigQuery)
- Engineer our back-end to support a highly scalable distributed data platform
- Work on building fast & robust microservice architecture
- Write and optimize advanced SQL queries to analyze the media analytics and financial data
- Report directly to our CTO and work closely with other Founders
- Fluent in writing high-quality code in Go
- Fluent in writing SQL queries
- Comfortable working using Docker & Kubernetes
- Well-versed in distributed databases & systems
- Self-organized and can work independently
Bonus if you are:
- Familiar with the Google Cloud Platform ecosystem
- Familiar with Airflow or Data flow
- Comfortable writing code in Python & using Jupyter notebooks
- Familiar with OLAP systems like Druid or Clickhouse
- Passionate about music and other media industries
- Have worked part of a remote team before
- Interested in blockchain tech
Our hiring process
- screening (20 mins)
- technical discussion (45-60 mins)
- interview (30 mins)