Onchain Data Analyst
Builds queries, dashboards, and analysis that explain wallet behavior, protocol performance, cohorts, token flows, and onchain user activity.
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Also listed as
Blockchain Data Analyst · On-chain Analyst · Web3 Data Analyst
What this role actually does
Most days, the practitioner has to write and test queries, investigate anomalies, update dashboards, and answer stakeholder questions.
Its decision rights usually cover data definitions, querying, and analysis, while attributing identity without evidence and formal investigations by default remain outside the default remit.
Where the role sits
Onchain Data Analyst usually sits inside Research, Data, Strategy, Economics, Risk, Investments, or ecosystem intelligence teams. Common reporting lines include Head of Research, Chief Economist, Data Lead, Strategy Lead, or Protocol Lead. Core-team and research-firm roles are common. Independent research, consulting, contributor work, and commissioned reports also exist. The role usually collaborates with DeFi Analyst, Tokenomics Analyst, Growth Manager, Ecosystem Researcher.
Core responsibilities
- Define metrics and the entities, events, contracts, and time windows behind them
- Write SQL or analysis code against indexed blockchain data
- Validate queries against explorers, protocol logic, and known edge cases
- Segment wallets, cohorts, assets, and behaviors without treating every address as a unique human
- Build dashboards and decision-ready reports
- Explain caveats such as bots, contracts, bridges, multi-wallet behavior, missing labels, and chain reorgs
Daily, weekly, and reactive work
A typical day
Write and test queries, investigate anomalies, update dashboards, and answer stakeholder questions.
Weekly or monthly
Review metric quality, publish an analysis, improve reusable data models, and align definitions with product or research teams.
When conditions change
Investigate broken pipelines, contract migrations, suspicious activity spikes, indexer delays, or metrics invalidated by protocol changes.
Deliverables
How success is judged
- Correct definitions
- Reproducible queries
- Decision usefulness
- Clear caveats
- Fewer conflicting internal metrics
- Timely detection of anomalies
Read signals in context. Read correct definitions together with reproducible queries. Neither signal is meaningful without the relevant launch, incident, market, workload, or attribution context.
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.
Skills and prerequisite knowledge
Hard skills
- SQL
- Blockchain event and contract literacy
- Data visualization
- Cohort analysis
- Statistical caution
Working skills
- Intellectual honesty
- Precision
- Clear uncertainty language
- Independent judgment
- Ability to change a view when evidence changes
Prerequisite knowledge
Know transactions, logs, addresses, contracts, token standards, protocol-specific events, and the distinction between address, account, and user.
Expectations by level
Entry level
At entry level, a candidate should be able to complete a scoped assignment with review. That includes the ability to define metrics and the entities, events, contracts, and time windows behind them, to write SQL or analysis code against indexed blockchain data, and to produce reviewable artifacts such as a SQL query set and a dashboard.
Mid level
At mid level, the practitioner normally owns data definitions, querying, and analysis without constant supervision. They can coordinate adjacent teams and improve the workflow behind a SQL query set and a dashboard, including when the role must investigate broken pipelines, contract migrations, suspicious activity spikes, indexer delays, or metrics invalidated by protocol changes.
Senior
At senior level, the work shifts toward standards, decision rights, and review quality. A senior Onchain Data Analyst defines how data definitions, querying, and analysis are handled, reviews high-risk cases, and builds systems that do not depend on one person.
Proof of work and portfolio
Reviewers should be able to inspect a SQL query set and a dashboard, trace the inputs or decisions behind the work, and understand what the candidate personally owned.
Strong proof
- A cohort dashboard
- A wallet-behavior study
- A metric dictionary
- A reproducible protocol report with query links
Weak evidence
- Dashboards with no definitions
- Wallet counts called users
- Charts built from copied queries the author cannot explain
Common mistakes and misconceptions
- Taking responsibility for attributing identity without evidence and formal investigations by default without the mandate or approval to do so
Common misconception
Onchain Data Analyst may overlap with DeFi Analyst, but the hiring evidence is different. This role is judged on data definitions, querying, and analysis, not on ownership of attributing identity without evidence and formal investigations by default.
Scope boundaries
Usually owns
- Data definitions
- Querying
- Analysis
- Dashboard design
- Stakeholder interpretation
- Methodology documentation
Usually does not own
- Attributing identity without evidence
- Formal investigations by default
- Protocol economic design
- Data engineering infrastructure unless assigned
- Trade recommendations
Interview focus
Expect questions about SQL, blockchain event and contract literacy, and data visualization, plus a scenario where the role must investigate broken pipelines, contract migrations, suspicious activity spikes, indexer delays, or metrics invalidated by protocol changes. Interviewers are looking for evidence that the candidate knows where data definitions and querying stop and attributing identity without evidence and formal investigations by default begin.
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?
Compensation and role risks
Direct role-specific salary evidence is sparse and titles vary. Use direct data or analytics listings only when blockchain scope matches. Otherwise use adjacent evidence and avoid numeric presentation.
No reliable role-specific range
KRAFT did not find a reliable role-specific range that meets the evidence standard. Compensation may still exist through salary, contract fees, retainers, grants, commissions, token or equity packages, creator revenue, or business economics. These models are described separately rather than compressed into an invented number.
Wider Web3 market, for scale
Typical advertised averages $65,000 – $200,000 / year
Individual postings run from about $40,000 to $350,000.
Across the role categories this index tracks, advertised averages sit between roughly $65,000 and $200,000 per year, with individual postings from about $40,000 to $350,000. This is whole-market scale from advertised roles - not a figure for this specific role, and not verified paid compensation.
Role risks
- Incorrect identity assumptions
- Data-source outages
- Metric gaming
- Stakeholders demanding certainty from incomplete labels
- Maintenance of brittle queries
Compensation can change materially by geography, seniority, employment model, company stage, market cycle, and the mix of cash, bonus, commission, equity, token, vesting, royalties, or fees. A published range is useful only when those dimensions match the role being considered.
How to read compensation evidence
- Direct
- Evidence from the same or a materially equivalent role.
- Adjacent
- Evidence from a neighbouring occupation, used only for context.
- Broad market
- Category-level Web3 or labour-market evidence.
- Unverified
- Estimates without enough source or methodology detail.
Confidence reflects the quality and comparability of the evidence, not the value or legitimacy of the role.
Career path and role fit
Common progression
May fit people who
People who like precise definitions, reproducible work, and explaining why a clean chart can still support a bad conclusion.
May not fit people who
People who dislike debugging data or who want dashboards to provide automatic truth.
Practical next steps
How this guide is built. Role content is drawn from current first-party hiring material and reputable industry evidence, with compensation labelled by confidence and evidence tier rather than a single number.
Turn this role into evidence.
Choose a proof-of-work project, package the result, and practice the questions this role is likely to ask.