| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
NEAR | San Francisco, CA, United States | $54k - $90k | |||
Zash | Remote | $50k - $70k | |||
Consensys | Remote | $81k - $87k | |||
Binance | Asia |
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| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Chainlink Labs | New York, NY, United States |
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Chainlink Labs | Remote |
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Ava Labs | New York, NY, United States | $46k - $80k | |||
Pocket Worlds | London, United Kingdom |
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Pocket Worlds | Austin, TX, United States |
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CoinMarketCap (CMC) | $32k - $72k | ||||
MetaMask | United States | $81k - $87k | |||
Ripple | San Francisco, CA, United States | $76k - $10k | |||
NAHC Limited | Hong Kong, Hong Kong | $32k - $64k | |||
OKX | Hong Kong, Hong Kong | $25k - $60k | |||
Braintrust | San Francisco, CA, United States |
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This job is closed
About The Role:
Pagoda is the first-ever Web3 Startup Platform where developers and entrepreneurs can build, launch, and operate new blockchain-based products and services. As a primary builder of the NEAR Protocol, Pagoda delivers capacity, velocity, simplicity, and affordability needed to power tomorrow’s Web3 startups.
The Pagoda Data Platform team is looking for a Data Engineer to build and scale our data infrastructure to empower our core products and product analytics.
What You'll Be Doing:
- Architect and manage data lakes and data marts needed to provide product analytics insights to product teams and executives (BigQuery, DataBricks);
- Create and manage data pipelines for both on-chain (NEAR) and off-chain (OLTP databases, http logs, UI analytics) data;
- Automate data quality monitoring and alerting tools;
- Optimize time to insight and work with Data Scientist to create data marts for various data products;
- Creating data extraction tools using Python, JavaScript, SQL, and Rust;
- Collaborate with internal and external engineers and product managers.
What We're Looking For:
- Experience building and managing data lakes aggregating dozens of data sources and providing insights to multiple different stakeholders based on terabytes of data;
- Experience in GCP and/or AWS data infrastructure products;
- Fluency in writing complex analytical SQL queries;
- Strong communication and remote friendly working skills;
- Bachelor’s Degree in Computer Science, Applied Mathematics or related field is a must.
We'd Love If You Have:
- Deep understanding of DataBricks and BigQuery technologies;
- Knowledge of product analytics tools such as Segment, FullStory, MixPanel or Amplitude;
- Familiarity with crypto or blockchain technologies;
- Experience working at a startup.
Here’s What Our Interview Process Looks Like:
Depending on calendar availability, from the first stage to the final stage, we do our best to keep the entire process to under three weeks. Our interviews take place via Zoom and typically consists of the following stages:
- Internal Recruiter Call (30 to 45 minutes)
- Technical Interviews (4 x 60 minutes)
- Pagoda Values Interview (30 to 45 minutes)
Please let us know if you require any special requirements for your interview and we’ll do our best to accommodate.
Ideal Location For This Role
This is a fully remote role, so that your timezone matches or overlaps with our leadership for this role, you’ll ideally be located in Americas or Europe.
What does a data scientist in web3 do?
A data scientist in web3 is a type of data scientist who focuses on working with data related to the development of web-based technologies and applications that are part of the larger web3 ecosystem
This can include working with data from decentralized applications (DApps), blockchain networks, and other types of distributed and decentralized systems
In general, a data scientist in web3 is responsible for using data analysis and machine learning techniques to help organizations and individuals understand, interpret, and make decisions based on the data generated by these systems
Some specific tasks that a data scientist in web3 might be involved in include developing predictive models, conducting research, and creating data visualizations.