Data Science Web3 Jobs in San Francisco, California, United States

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Job Position Company Posted Location Salary Tags

PIP Labs

San Francisco, CA, United States

$90k - $100k

a16z

San Francisco, CA, United States

$225k - $250k

Markets

San Francisco, CA, United States

$88k - $110k

Bitwise Asset Management

San Francisco, CA, United States

$110k - $125k

Whatnot

San Francisco, CA, United States

$153k - $225k

Kraken

San Francisco, CA, United States

$76k - $90k

Whatnot

San Francisco, CA, United States

$180k - $275k

Kraken

San Francisco, CA, United States

$105k - $115k

Ripple

San Francisco, CA, United States

$219k - $219k

Cash App

San Francisco, CA, United States

$79k - $84k

Whatnot

San Francisco, CA, United States

$180k - $230k

0x

San Francisco, CA, United States

$36k - $54k

Ripple

San Francisco, CA, United States

$73k - $93k

Heretic

San Francisco, CA, United States

$105k - $150k

CoinList

San Francisco, CA, United States

$150k - $207k

PIP Labs
$90k - $100k estimated
SF Bay Area
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Data Engineer

SF Bay Area
Engineering /
Full Time /
Hybrid

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PIP Labs is a research and engineering company focused on decentralized technologies, intellectual property (IP), and artificial intelligence (AI). As an initial core contributor to the Story Network, PIP Labs develops infrastructure and technical solutions that connect the potential of AI with sustainable ownership models, supporting IP holders, creators, and innovators.

About the Role

We’re seeking a Data Engineer to join our team and lead the development of critical data infrastructure. You’ll build systems that give real-time insight into token activity, exchange dynamics, and ecosystem health. This role is perfect for someone who thrives in early-stage environments, is excited by messy data, and wants to help define how web3 data is captured, visualized, and acted on.

Key Responsibilities

    • Design, implement, and maintain data pipelines to collect, clean, and transform raw on-chain and off-chain data into structured formats.
    • Build and maintain dashboards to monitor exchange floats across centralized and decentralized platforms.
    • Track token movements in and out of exchanges in real time.
    • Analyze daily token inflow and outflow patterns.
    • Identify and surface high-volume transactions across DeFi projects.
    • Monitor significant token transfers and wallet-level activity.
    • Map and track token flows across interrelated wallet clusters.
    • Collaborate with engineering, product, and leadership teams to define key metrics and translate business needs into scalable data solutions.
    • Ensure data quality, accuracy, and reliability through rigorous testing, validation, and documentation practices.
    • Develop automated reporting tools and ad hoc queries to support strategic decision-making.
    • Recommend improvements to existing infrastructure, tooling, and workflows as we scale.

What We’re Looking For

    • 3+ years of experience as a Data Engineer or in a similar role building production-grade data pipelines and dashboards
    • Strong understanding of DeFi protocols and the ability to monitor and analyze smart contract activity across DeFi projects
    • Proficiency in SQL, Python, and working with APIs (especially blockchain or web3-related APIs)
    • Experience with data visualization tools like Looker, Tableau, Metabase, or similar
    • Familiarity with on-chain data sources and blockchain analytics (e.g., Dune, Flipside, Nansen)
    • Strong understanding of data modeling, ETL processes, and warehouse design (e.g., Snowflake, BigQuery, Redshift)
    • A collaborative mindset, clear communication skills, and the ability to work autonomously in a fast-paced environment
    • Curiosity and passion for the web3/crypto space — you don’t need to be an expert, but some fluency helps

Nice to Have

    • Experience tracking token liquidity and market maker flows
    • Prior work with decentralized exchange (DEX) or DeFi protocol data
    • Background in financial modeling or working with trading/investment teams
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