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All capabilities · Data engineering & analytics

Build dashboards that answer business questions

Power BI/Tableau/Looker Studio: model data, KPIs, drill-downs, publish and present.

~20 focused hoursbeginner
Explore 3 tools for this project
Market relevance

Which roles ask for this — and how often

Share of job postings in India, per role, that name this capability.

AI-enabled Data Analyst98%AI Technical Trainer / Instructor46%AI Search Optimization Specialist33%
What employers mean

You should be able to…

  1. Model a star schema (fact + dimension tables) from raw exports before building visuals
  2. Write basic-to-intermediate DAX or calculated fields for KPIs (YoY growth, running totals)
  3. Build drill-down dashboards a non-technical stakeholder can self-serve
  4. Connect live data sources (database, Google Sheets, API) rather than static file uploads
  5. Design for the audience: pick the right chart type, avoid clutter, highlight the one number that matters
  6. Publish and share a dashboard with proper access/refresh scheduling
  7. Present a dashboard live and answer 'why' questions the visuals alone don't explain

Needs first: Query and model data with SQL

Learn — free, link-checked

The few resources that matter

Read · beginner · 30 min · support.google.com

Get started with Looker Studio

Google's own guide to connecting data sources and building interactive reports in a tool that's free forever, no license needed. — Google
Watch · beginner · 40 min · youtube.com

How to use Microsoft Power BI - Tutorial for Beginners

Concise, screen-recorded build of a real Power BI report from a former Microsoft PM — good model for a portfolio dashboard. — Kevin Stratvert
Course · beginner · 180 min · learn.microsoft.com

Power BI training

Official, free learning path covering data modeling, DAX basics, and publishing dashboards — the exact skill 18 job posts named. — Microsoft Learn
Read · beginner · 180 min · clauswilke.com

Fundamentals of Data Visualization

The free book on which chart to use and why — directional colour, avoiding distortion, annotating the point. Teaches judgment, not tool clicks. — Claus O. Wilke
Tools for practice

Choose a tool for the job

Start with one tool for each part of your project. You don’t need to learn them all.

Go to the practice brief

3 tools to explore

Practices & references

  • SQL
  • DAX
  • Metric definitions

Selected for practice by RoleGPS. No paid placements or affiliate links.
Access can change; check the details before starting.

Think your tool is a good fit? Let us know

Practice

Star-schema retail dashboard with city and category drill-down

Take the Superstore sales export — a single flat sheet of orders with city, region, category, ship mode and order/ship dates — and model it properly into fact and dimension tables before a single visual is drawn. Build a Power BI or Looker Studio report with a KPI summary page and two drill-down pages (by city, by shipping mode), deriving an on-time flag from the order-to-ship gap. The point of the project is the model underneath: measures like % on-time, MoM growth and repeat-customer rate should fall out of the schema rather than being hacked per-visual.

Start from

Superstore sales dataset on Kaggle — ~10k orders with city, region, category, ship mode and order/ship dates

Milestones
  1. Split the flat sheet into fact + date/customer/product/geography dimensions and wire the relationships · ~4h
  2. Build the KPI summary page with the on-time, MoM growth and repeat-customer measures · ~4h
  3. Build the two drill-down pages and make cross-page filtering work from the summary · ~5h
  4. Publish the report and record the walkthrough of the model and design choices · ~3h
Done when
  • Data is modeled as fact + dimension tables, not one wide flat sheet
  • At least 3 DAX/calculated measures beyond simple SUM (e.g. % on-time, MoM growth, repeat-customer rate)
  • Drill-down works: clicking a city on the summary page filters the detail page
  • Published/shareable link or a 3-5 min recorded walkthrough explaining design choices
Prove it

Evidence a recruiter can check

  • A published, openable dashboard link where clicking a city on the summary page visibly filters the detail page
  • The data-model diagram showing fact and dimension tables with relationship cardinality — and a note on why the flat sheet was split
  • Every calculated measure written out with its DAX/formula and the business definition it encodes (what counts as 'on time')
  • A recorded walkthrough presenting the dashboard the way you would to a stakeholder, not a feature tour
Signal it

Built a published Power BI dashboard on a 10k-order retail dataset — modelled it into a star schema, defined on-time, MoM-growth and repeat-customer measures, and wired city-level drill-down from the summary page.

Interview

Questions you'll get asked

  1. Walk me through how you'd design a dashboard for a sales team from scratch — what do you ask first?
  2. What's the difference between a calculated column and a measure in Power BI/DAX?
  3. How do you handle a dashboard that's slow because the underlying data model is a mess?
  4. When would you use a line chart vs. a bar chart vs. a table — and when should you NOT use a pie chart?
  5. How do you set up row-level security so different regional managers see only their data?
  6. A stakeholder asks for '20 more metrics' on one page — how do you push back constructively?
  7. How would you set up scheduled refresh for a dashboard fed by a live SQL database?
See where you stand for AI-enabled Data Analyst