What does a Data Visualization Engineer do?
A Data Visualization Engineer turns raw data into clear, interactive visuals that help people make decisions. You bridge analytics, design, and engineering to ship dashboards, reports, and data apps that are fast, accessible, and trustworthy.
- Translate business questions into metrics and charts.
- Build interactive dashboards and data apps using BI tools or web stacks.
- Model, query, and transform data for visual display.
- Apply design principles for clarity, layout, and color.
- Optimize performance, ensure accessibility, and version control your work.
Typical deliverables
- Executive dashboard with KPIs and drilldowns
- Exploratory analysis notebook with compelling visuals
- Design system tokens for charts (color, typography, spacing)
- Reusable visualization components or templates
- Documentation: metric definitions, interaction notes, and how-to guides
Day in the life (example)
- 9:00 — Review stakeholder questions and yesterday’s dashboard usage
- 10:00 — Refine SQL for a new metric; validate with sample queries
- 11:30 — Iterate on chart layout and annotations
- 13:30 — Implement filters and drill-through interaction
- 15:00 — Performance pass: caching, index checks, query tuning
- 16:00 — Peer review and documentation updates
Who this is for
- Analytically minded people who enjoy turning questions into measurable visuals.
- Design-aware developers who care about clarity, UX, and speed.
- BI analysts ready to deepen engineering and design skills.
Prerequisites
- Comfort with basic statistics (mean, median, distributions).
- Foundational SQL (SELECT, WHERE, GROUP BY, JOIN).
- Basic scripting in Python or JavaScript is helpful but not required on day one.
- Willingness to iterate with feedback and document decisions.
Quick self-check
- Can you explain when to use a line vs. bar vs. scatter?
- Can you write a SQL query to aggregate by day and calculate a rolling average?
- Can you describe what makes a dashboard fast or slow?
Where you can work
Data Visualization Engineers are hired by:
- Product and Growth teams (activation, retention, funnels)
- Finance and Sales Ops (revenue, pipeline, forecasting)
- Marketing Analytics (campaign performance, attribution)
- Operations and Supply Chain (inventory, logistics, SLAs)
- Healthcare, Public Sector, Education (public dashboards and data apps)
Hiring expectations by level
Junior
- Implements charts from well-defined specs.
- Writes clean SQL with guidance; basic performance awareness.
- Applies a design system; learns accessibility basics.
- Portfolio shows 2–3 end-to-end dashboards or data stories.
Mid-level
- Owns features end-to-end: scoping, data modeling, visuals, QA.
- Introduces reusable components; sets chart standards.
- Proactively handles performance and accessibility.
- Communicates trade-offs with stakeholders.
Senior
- Leads visualization strategy, governance, and design systems.
- Partners with data engineers to shape semantic layers.
- Mentors others; sets review processes and performance budgets.
- Owns cross-team dashboards with reliability and documentation.
Salary ranges
- Junior: ~$60k–$90k USD
- Mid-level: ~$90k–$130k USD
- Senior: ~$130k–$180k+ USD
Varies by country/company; treat as rough ranges.
Skill map (with mini tasks)
Use these mini tasks to practice the core skills. Keep results in a single portfolio repo or folder.
Data Visualization Theory
- Mini task: Redesign a cluttered chart into a clear version. Annotate why your changes help.
- Focus: chart selection, scale choice, color use, encoding hierarchy.
Interactive Dashboards
- Mini task: Build a 2-page dashboard with filters and a detail drill-through.
- Focus: KPI cards, responsive layout, state handling.
SQL Basics
- Mini task: Create a query that outputs daily active users, 7-day rolling average, and WoW change.
- Focus: joins, window functions, date handling.
Scripting (Python or JS)
- Mini task: Reproduce one chart in code with a config-driven approach.
- Focus: data transforms, reusable configs, simple tests.
Design Systems
- Mini task: Define tokens for color, spacing, typography; apply to 3 charts.
- Focus: consistency, accessibility contrast ratios.
Accessibility
- Mini task: Add alt text, keyboard navigation, and high-contrast mode to one dashboard page.
- Focus: labels, tab order, colorblind-safe palettes.
Storytelling with Data
- Mini task: Create a one-page narrative with 3 charts and a clear call to action.
- Focus: sequencing, annotations, audience framing.
Prototyping
- Mini task: Wireframe a dashboard on paper or tool, then test with a colleague before building.
- Focus: scope clarity, fast feedback, iteration.
Performance Considerations
- Mini task: Reduce dashboard load time by 30% (indexes, caching, query pruning).
- Focus: budget, instrumentation, regression checks.
Version Control
- Mini task: Put all assets under Git, use branches, and open a self-review PR with screenshots.
- Focus: reproducibility, reviewability.
Learning path
- Week 1–2: Foundations
- Study Data Visualization Theory and SQL Basics.
- Mini deliverable: a metric SQL query and 2 redesigned charts.
- Week 3–4: Interactivity + Scripting
- Build an Interactive Dashboard and reproduce one chart with Python or JS.
- Mini deliverable: 2-page dashboard with filters and a code-based chart.
- Week 5: Design System + Accessibility
- Create chart tokens and ensure contrast/keyboard support.
- Mini deliverable: token doc + accessible dashboard pass.
- Week 6: Storytelling + Prototyping
- Draft a narrative data story; test with a user; refine.
- Mini deliverable: one-page story with annotations.
- Week 7: Performance + Versioning
- Add performance budget, cache strategy, and Git workflow.
- Mini deliverable: before/after timings and PR with change log.
Practical projects (portfolio-ready)
- E-commerce Growth Dashboard: Activation, conversion, retention, and revenue with cohort view. Outcome: executive view + analyst drill-down.
- City Mobility Explorer: Trips, duration, station capacity, time-of-day flows. Outcome: interactive map + peak-hour heatmap.
- Marketing Attribution Story: Channel mix, assisted conversions, and CAC trend. Outcome: narrative slide with 3 charts and action items.
- Operational SLA Monitor: On-time rate, backlog, aging, alerting thresholds. Outcome: real-time dashboard with performance budget.
- Public Health Summary: Cases, positivity, demographics, and geography. Outcome: accessible, mobile-friendly public dashboard.
Project tips
- Include README with metric definitions and data caveats.
- Show before/after performance timings.
- Add screenshots and a short user guide.
Interview preparation checklist
- Explain a metric precisely (numerator/denominator, filters, grain).
- Choose the right chart for a scenario and justify trade-offs.
- Write an efficient SQL query with a window function.
- Refactor a chart using a design system token set.
- Discuss accessibility improvements and how you test them.
- Walk through a performance optimization you shipped.
- Show a project repo with clear commits and a PR.
Practice prompts
- Redesign this dual-axis chart into a safer alternative.
- Stakeholder asks for 20 filters: push back or implement? Why?
- Dashboard is slow at 9 a.m. only. What do you investigate?
Common mistakes and how to avoid them
- Overdecorating charts: prioritize message, reduce ink, add annotations.
- Unclear metrics: define grains, filters, and edge cases; document them.
- Ignoring performance: set budgets, cache, and test with realistic data.
- Color-only encoding: add labels, patterns, or shapes; use contrast-safe palettes.
- No version control: keep dashboards, queries, and exports tracked.
- Skipping user feedback: prototype early; test with 2–3 target users.
Exam access
The Core Exam below is open to everyone. If you log in, your progress and score are saved automatically.
Next steps
Pick a skill to start and complete the first mini task. Then build your first dashboard and iterate with feedback. When ready, take the exam to validate your knowledge. Use the Go to skills button to jump to the skills section.