NFT Analytics Engineering Assignment

Overview

Build an analytics pipeline that processes NFT transfer events and wallet data using provided dbt and Dagster environment. Expected time: 3-4 hours.

Data Sources

Run these in the order they're listed below

• logs_nft1.sql
• logs_nft2.sql
• txs_nft1.sql
• txs_nft2.sql

wallet_networth.csv is a csv file with each holder's wallet native balance on ethereum.

Import this data into your db in another table to then run the analysis pipelines on them.

Required Analytics Models

1. Core NFT Ownership (Primary Focus)

Create models showing:

• Current snapshot of all NFT holders
• Incrementally updates for faster processing
• Includes total tokens held per wallet
• Includes first/last transfer timestamps per wallet

2. Trader Classification

• Whales: Wallets holding >1% of supply
• High-Value Holders: Wallets holding ≥2 NFTs AND net worth >$5k
  • We should be able to easily paramterize the NFT and net worth number and rerun the analysis on the new parameters

3. Data Quality

Implement basic dbt tests to verify:

• Transfer logic correctness
• Holdings calculations
• Edge cases (mints/burns)

4. Simple Refresh Pipeline

Create a Dagster job that:

• Refreshes current holders and trader classifications
• Runs every 5 minutes
• Includes basic logging

Deliverables

1. DBT Models

   • Analytics models
   • Basic tests

2. Dagster Pipeline

   • Basic scheduled job
   • Error handling

3. README explaining:

   • Approach
   • How to run
   • How to validate

Validation

Compare against Etherscan:

• https://etherscan.io/token/0xc2e9678a71e50e5aed036e00e9c5caeb1ac5987d
• https://etherscan.io/token/0x1b41d54b3f8de13d58102c50d7431fd6aa1a2c48