Back to portfolio
Research & DataSimulated project
Onchain Data Analyst portfolio brief
A simulated research & data exercise for Onchain Data Analyst. Treat it as realistic practice, not real client or protocol work.
6 deliverables6 tools referenced3 linked interview questions
Objective
Rebuild one public metric from raw events
Expected deliverables
SQL query setDashboardWallet or cohort studyProtocol performance reportMetric dictionaryMethodology note
Recommended workflow
- Rebuild one public metric from raw events
- Write the definition and known failure cases
- Create a decision memo that uses the data without overstating it
- Review the result against the rubric, then package it as a case study.
Constraints and safety
- Use only public sources or clearly simulated data.
- Never include seed phrases, private keys, or wallet secrets.
- Redact private user, company, security, legal, or compensation information.
- State every assumption you make.
Tools in practice
- Dune
- Define protocol events and wallet cohorts, write reproducible queries, validate results against explorers, and publish dashboards with metric definitions.
- Flipside
- Define protocol events and wallet cohorts, write reproducible queries, validate results against explorers, and publish dashboards with metric definitions.
- SQL
- Define protocol events and wallet cohorts, write reproducible queries, validate results against explorers, and publish dashboards with metric definitions.
- Python
- Define protocol events and wallet cohorts, write reproducible queries, validate results against explorers, and publish dashboards with metric definitions.
- block explorers
- Verify transactions, contract addresses, events, token movements, deployment state, and incident claims against chain data.
- data warehouses or notebooks
- Define protocol events and wallet cohorts, write reproducible queries, validate results against explorers, and publish dashboards with metric definitions.
What a strong submission shows
- A cohort dashboard
- A wallet-behavior study
- A metric dictionary
- A reproducible protocol report with query links
Weak submission patterns
- Dashboards with no definitions
- Wallet counts called users
- Charts built from copied queries the author cannot explain
Review rubric
- Accuracy of claims and sources
- Relevance to the target role
- Decision quality and trade-offs
- Completeness of the deliverable
- Clarity of communication
- Honest limitations and next steps
Present it as a case study
- Explain the problem, sources, decisions, ownership, constraints, output, review, and what you would change.
- Show the final artifact before the process notes.
- State what you owned and what belonged to other people.
- Label the work as simulated, and keep any real contribution clearly separate.
Interview questions this project helps you answer
How do you validate a query when the dashboard result looks plausible?
Why is address count a weak user metric?
How would you investigate a sudden increase in active wallets?