Analytics Manager - Blockchain & Web3

Serotonin•New York, NY

44d•Hybrid

About The Position

We're looking for a versatile data professional who combines strong analytical and data science capabilities with the engineering skills to build and maintain the infrastructure behind those analyses. This is a hybrid role perfect for someone who loves both discovering insights and building scalable systems to deliver them. You'll work across the entire data stack - from pipeline development to statistical modeling.

Requirements

Strong experience with modern data stack tools (e.g., dbt, Airflow/Dagster, Snowflake, BigQuery, Redshift, or similar)
Proven ability to design and manage ETL pipelines and database architectures
Advanced SQL skills and high proficiency in Python for both data analysis and engineering
Understanding of data modeling principles (Kimball, Data Vault, or similar)
Experience with cloud data platforms (AWS, GCP, or Azure)
Strong statistical analysis skills with hands-on experience using Python data science stack (pandas, NumPy, scikit-learn)
Experience with A/B testing, causal inference, and experimental design
Ability to communicate complex findings to non-technical stakeholders
Track record of using data to influence business strategy
Hands-on experience with blockchain data extraction, transformation, and analysis
Understanding of EVM concepts (transactions, events, smart contracts) and/or non-EVM ecosystems
Ability to work with on-chain data to derive actionable insights

Nice To Haves

Experience with marketing analytics and attribution modeling
Familiarity with data from providers like Dune, Arkham, GoldSky, Flipside, or similar
Previous experience in data roles within blockchain/Web3 organizations
Experience with data visualization tools (Tableau, Looker, or similar)
Gets excited about both building robust data infrastructure AND discovering insights
Wants ownership over the entire data value chain
Thrives in environments where they can wear multiple hats
Values being able to see their analyses through to production impact

Responsibilities

Build and maintain data pipelines that power both ad-hoc analyses and production dashboards
Develop statistical models and data science solutions while also implementing the infrastructure to deploy them
Create self-serve analytics tools and datasets that empower stakeholders across the organization
Design experiments and perform statistical analyses to measure product and marketing initiatives
Build data models in our warehouse that balance analytical flexibility with performance
Partner directly with product, marketing, and leadership teams to identify opportunities and measure impact
Own the full lifecycle of data products - from initial exploration to production deployment