Hire Tableau Data Analyst Intern — the employer’s brief

The three that matter most are Checking a number against a known total before sending it; Explaining a finding in three sentences to someone who will not read the appendix; Publishing to Server or Cloud. Beyond those, look for working familiarity with Tableau Desktop, Tableau Server or Cloud, SQL. Everything else on the list above is teachable inside a term — treating it as an entry requirement shrinks your pool without improving the hire.

Yes, within a scoped brief. Audit one dataset for duplicates, nulls and reconciliation gaps and publish the findings is achievable in a term with weekly review, and it is genuine output rather than a training exercise. What does not work is open-ended ownership of anything with production consequences — keep the judgement calls with the reviewer and the execution with the intern.

It is the bottom of the working band, and appropriate for a smaller city or a shorter commitment. In Bengaluru, Hyderabad, Pune, Mumbai or the NCR, expect to be closer to ₹30,000 for the same skills — you are competing with every other employer for the same few candidates. Decide where in the ₹13,000–₹30,000 band you sit before the first interview rather than during the offer call.

Ask "When do you use an extract instead of a live connection?" — you are listening for practical experience with performance. Then follow the example they give rather than moving on to your next question. Score every candidate on the same set so the shortlist stays comparable.

One finished, reviewed piece of work that someone on the team would otherwise have done — not a side project nobody adopts. The deliverables above are sized for eight to twelve weeks of supervised work by a student with the fundamentals but no production experience. If they can demo it and the team keeps using it after they leave, the hire paid for itself.

The "Data" qualifier means the candidate should be comfortable being wrong in public: reconciling, checking and correcting numbers. Screen for verification habits rather than tool familiarity. In screening terms, that means adding one specific check: checking a number against a known total before sending it.

A decision. An analyst’s output is a recommendation someone acts on, not a working artefact. Judge them on whether a real question got answered and whether the answer changed anything — the dashboard or the deck is just the delivery mechanism.

Always. Students plan around semester dates, and a listing without a start date and duration is filtered out by exactly the organised candidates you want. For Tableau roles, stating "three months, starting June" typically produces more applications than an open-ended listing at a higher stipend.

Specificity does most of the work. A listing that names the project, the tools and the deliverable filters itself, because candidates can tell whether they fit. Adding one screening question to the application — from the set above — removes most of the rest without adding a review round.