Portfolio Examples · Data Analyst Portfolio
Data Analyst Portfolio Example: A Sample SQL, Experimentation, and BI Dashboard Portfolio for 2026
This page is a fully built sample data analyst portfolio website — built on the same Gradient template a paying Seera client would publish. The layout, styling, and structure are byte-for-byte identical to what you would get if you published a data analyst portfolio on Seera tonight. The only difference is the data: instead of a real practitioner's work, this example uses a fictional but realistic profile (Priya Nair, a Chicago-based senior data analyst working on product and growth analytics in US e-commerce and B2B SaaS).
What you can take from this data analyst portfolio example: the section structure, how to present an A/B test alongside a dashboard build, what depth an analysis case study should have, and how to end every project with the decision it drove rather than the chart it produced.
View the Live Data Analyst Portfolio →
The data analyst portfolio at a glance
| Data Analyst | Priya Nair (fictional, modeled on a working pattern) |
| Specialty | Senior IC · SQL analysis · experimentation · self-serve BI |
| Industry | E-commerce, B2B SaaS, marketing analytics |
| Location | Chicago, IL — open to remote (US) |
| Years working | 7 years |
| Portfolio template | Gradient — modern, data-friendly, clean for tables and charts |
| Sections shown | Hero · About · Stack · 4 Analysis Case Studies · Experience · Education · Testimonials · Contact |
Why this layout works for a data analyst portfolio
The Gradient template is a deliberate choice for a data analyst portfolio. Three things about why it works:
- Clean, premium surface for numbers. Analysis case studies are full of metrics — conversion lift, request volume, budget reallocated — and the template keeps them legible instead of decorative.
- Reads as senior, not flashy. Hiring managers for lead-analyst roles read restraint as a signal; Gradient is polished and data-friendly without going developer-terminal.
- Case-study structure holds a decision. Every project on a strong data analyst portfolio ends with an action a stakeholder took — the layout gives that its own line rather than burying it under the chart.
Section-by-section breakdown of the data analyst portfolio
1. Hero — focus and current availability
The hero reads "Senior Data Analyst · SQL & BI Dashboards" — specific enough that a hiring manager for a product- or growth-analytics role knows immediately whether to keep reading.
2. About — operating style, not biography
The about section explains the operating style: decision-focused, a small curated set of analyses, and a habit of writing up experiment readouts so they can be rerun. It shows current availability above the fold.
3. Tools and methods — grouped by use case
For any data analyst portfolio, this section answers two practical questions: can they write the SQL and build the dashboards we use, and do they understand experimentation. The example breaks the stack into SQL & Analytics, Languages & Modeling, BI & Visualization, and Collaboration & Communication. The Collaboration & Communication grouping — stakeholder translation, data storytelling, self-serve enablement — is what separates a senior analyst from a ticket-taker.
4. Analysis case studies — decisions over dashboards
The case-study section is the strongest part of any data analyst portfolio. The example here shows four analyses across deliberately different categories:
- A checkout-funnel A/B testing program (+6.2% conversion, pre-registered, statistically significant)
- A self-serve Looker layer that cut ad-hoc data requests 60%
- A marketing-attribution analysis that reallocated a $1.4M annual budget
- A reproducible public SQL + dashboard project on the NYC 311 dataset
A data analyst portfolio of four dashboards shows you can build dashboards. A portfolio of four different kinds of work — an experiment, a self-serve build, a budget-moving analysis, and a public reproducible project — shows an analyst whose work changes decisions.
5. Work history — companies, roles, specific analyses
Each role on this data analyst portfolio leads with the most concrete thing the analyst did there: conversion lifted, requests reduced, budget reallocated, churn model shipped.
6. Testimonials — from a VP, a head of product, and an analytics peer
Three testimonials in this data analyst portfolio: a VP of Growth (the decision-maker), a former Head of Product (the budget owner), and an analytics-engineer peer (independent verification of the dbt and dashboard work).
7. Contact — one email, one clear next step
The contact section is intentionally simple: an email, GitHub, LinkedIn, and a clear note on availability.
What this data analyst portfolio gets right (and what to copy)
- Every project ends with a decision. Not "built a dashboard" but "finance reallocated $1.4M." That is the single highest-signal habit for a data analyst portfolio.
- Experimentation stated explicitly. "Pre-registered A/B test, statistically significant over 41 days" separates an analyst from someone who reads charts.
- Self-serve work treated as headline. The Looker layer that cut requests 60% shows the analyst scales a team, not just answers tickets.
- One reproducible public project. The NYC 311 analysis is verifiable and confidentiality-safe — the piece a hiring manager can actually click into.
- Range of categories. Experiment, dashboard, budget analysis, public project — signals what the analyst can be hired for.
How to build a data analyst portfolio like this for yourself
- Upload your CV or analyst bio to Seera. The AI extracts your analyses, tools, and roles.
- Pick the Gradient template (or browse the other 15 templates — Aurora for premium glass, Pulse for a data-vibe, Meridian for executive-facing reporting).
- Replace the four sample case studies with three to five of your own — at minimum one A/B test, one dashboard build, and one analysis that changed a decision.
- Write proper analysis case studies. Each needs the question, the SQL or method, the dashboard, the result, and the decision a stakeholder took.
- Get three named testimonials — a manager, a peer or analytics engineer, and a downstream stakeholder (a PM, marketer, or finance partner).
- Connect your custom domain on Pro.
priyanair.comsignals seriousness in a way a builder subdomain never will.
Frequently asked questions about data analyst portfolios
What does a good data analyst portfolio look like in 2026?
A strong data analyst portfolio leads with three to five analyses that each drove a decision — the question, the SQL logic, the dashboard, and the action a stakeholder took — rather than a wall of notebooks. Avoid: ten notebooks with no business context, screenshots with no query behind them.
What sections should a data analyst portfolio include?
Six core sections: (1) Hero; (2) About — operating style; (3) Analysis case studies — three to five with the question, method, dashboard, and decision; (4) Tools and methods — SQL, BI, and modeling grouped by use case; (5) Work history; (6) Testimonials and education.
What projects should a data analyst include?
Aim for one A/B-tested experiment, one self-serve dashboard build, one business-impact analysis where a budget or roadmap changed, and one reproducible public project on an open dataset. Early-career analysts should lead with two end-to-end analyses on public data with a written readout.
How is a data analyst portfolio different from a data scientist portfolio?
A data analyst portfolio is judged on decisions driven, not models deployed — SQL analyses, experiment readouts, and self-serve dashboards. A data science portfolio leans on applied ML and production deployment. The overlap is SQL and experimentation; the difference is the headline.
Which template style works best for a data analyst portfolio?
A clean, premium template that holds tables and numbers clearly. The example uses Gradient (modern, data-friendly). A premium-glass look may suit Aurora or Pulse; executive-facing reporting may suit a restrained template like Meridian.
How do I write a data analyst portfolio bio?
Answer four questions: what you analyze, the company type, the operating style, and current availability. Avoid: "passionate about data", "data-driven storyteller", "turning numbers into insights".
Related data analyst portfolio resources
- How to Build a Data Analyst Portfolio: Project Templates That Get Hired
- Compare with: Data Science Portfolio Example
- Compare with: Marketing Manager Portfolio Example
- Moving toward product? See a Product Manager Portfolio Example
- More requirements than queries? See a Business Analyst Portfolio Example
- Gradient Template — See the Default Layout
- All 15 Portfolio Templates (Free to Try)
- Best Portfolio Website Builders 2026