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Performance review template for a data analyst
A ready-to-use, section-by-section template with the competencies that matter for a data analyst, role-specific example phrases, and a guard against the stock filler that makes most reviews read as generic. Copy the structure, fill in your evidence, or skip the writing entirely with Crestento.
The template
Four sections, in this order. Length should match the evidence you have — a thin section is honest; an invented paragraph is not.
Summary
One or two paragraphs setting the context: what was expected of data analyst this period, and your overall verdict. Lead with the headline.
Example phrasing
“Owned the executive growth dashboard end-to-end across the year, ran the analysis that surfaced the onboarding-step-3 drop-off (which became the product team's Q3 priority), and rebuilt the marketing-attribution model that finance now uses for ROAS reporting.”
Strengths
The behaviours and outcomes that made the work happen. Anchor in evidence: analyses delivered that drove a documented decision, dashboards built and adoption signal, data-quality incidents caught vs caused.
- Evidence for: SQL fluency and data-modelling judgement.
- Evidence for: dashboard and reporting craft.
- Evidence for: ad-hoc analysis with clear business framing.
- Evidence for: data-quality discipline and pipeline awareness.
Areas for Growth
Forward-looking development edges. Frame as opportunities, not deficiencies. Specific behaviours to develop, not generic data analyst criticism.
- One pattern observed across the period.
- One specific behaviour to develop.
- One concrete next step.
Goals for the Next Period
Two or three concrete goals. Each should name a specific behaviour change, a measurable target, and a deadline. Avoid vague aspirations.
Competencies to evaluate
The 7 competencies a strong data analyst review structures around, in priority order. Use these as the spine of the Strengths and Areas for Growth sections.
- SQL fluency and data-modelling judgement
- dashboard and reporting craft
- ad-hoc analysis with clear business framing
- data-quality discipline and pipeline awareness
- stakeholder communication (translating findings)
- experimentation analysis (A/B test integrity)
- tooling depth (BI tool, warehouse, scripting)
Before you write
Strong data analysts produce work that CHANGES decisions. The craft is in framing the right question, doing rigorous analysis with clear caveats, and communicating the finding in a way stakeholders can act on. Weak analysts produce dashboards no one looks at and analysis that just describes what already happened. The dashboard-built count is the floor; the decision-changed count is the ceiling.
Evidence to gather
Strong reviews for a data analyst cite evidence of these shapes. Only use a specific value (a percentage, a count, a dollar amount) if you actually have it — don’t invent a number to sound concrete.
- analyses delivered that drove a documented decision
- dashboards built and adoption signal
- data-quality incidents caught vs caused
- experimentation tests analysed with correct statistical handling
- stakeholder-requested-analysis turnaround time
Where to find the evidence
Work products a data analyst produces. Reference these by name in the review when they’re relevant — it signals you know the work.
- executive / business dashboards (Looker, Tableau, Sigma, etc.)
- ad-hoc analysis decks for leadership
- SQL queries / dbt models in the warehouse
- experimentation readouts (with stats methodology)
- data-quality audit reports
- documentation of metric definitions
Phrasing that lands vs phrasing that doesn’t
Strong — specific, evidenced, role-appropriate
“Owned the executive growth dashboard end-to-end across the year, ran the analysis that surfaced the onboarding-step-3 drop-off (which became the product team's Q3 priority), and rebuilt the marketing-attribution model that finance now uses for ROAS reporting.”
Weak — vague, unevidenced, generic
“Strong analyst, good with data.”
Phrases to never use
Stock filler that AI-written data analyst reviews slip into. Managers spot it instantly. Rewrite to name a specific behaviour instead.
- “data-driven mindset”
- “great with numbers”
- “strong analyst”
- “passionate about data”
- “go-to person for analytics”
- “consistently delivers insights”
- “trusted partner to the business”
- “tells a story with data”
Don’t invent these specifics
The details an AI tends to fabricate for data analystreviews. If you don’t have the specific number, name, or date in your notes, leave it out — generic-but-honest beats specific-but- invented every time.
- specific dashboard names or business areas not in input
- named analyses or projects when only general work was mentioned
- specific decision-impact claims not provided
- particular tools (Looker, Snowflake) not referenced
- specific data-quality incidents not in input
- experimentation results or statistical methods not mentioned
Skip the template, generate the review
Drop your bullet points into Crestento and it produces the polished draft using this exact template structure, tuned for a data analyst. Two reviews free, no card.
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