Basic SQL screen rep

Know what "basic SQL" means before the interview.

For a data analyst screen, basic SQL is not a trivia list. You need to turn a business question into the right row grain, join path, grouped result, and sanity check without hiding behind syntax.

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Prompt

An interviewer says the role needs "basic SQL." What should you be ready to do, and how would you prove it with a short practice set?

Answer shape

  • State the business question before the syntax.
  • Name the row grain: one row per customer, order, event, account, ticket, or day.
  • Use joins, grouping, `CASE`, null handling, date filters, and CTEs in realistic combinations.
  • End with a sanity check so the interviewer trusts the metric.

Run the 30-minute version.

  • Minutes 0-5: write down the tables you expect: `users`, `orders`, `refunds`, `events`, or `tickets`.
  • Minutes 6-15: solve monthly active buyers, repeat purchase rate, average order value, top customers excluding refunds, and orders by signup cohort.
  • Minutes 16-22: explain each output grain and how nulls, duplicates, many-to-many joins, or refunded orders could break the result.
  • Minutes 23-30: say the answer out loud. If you cannot explain the grain in one sentence, rewrite the query.

What to be ready for

  • `SELECT`, `WHERE`, `ORDER BY`, and `LIMIT`.
  • Inner joins and left joins, including what drops from the result.
  • `GROUP BY`, `HAVING`, and aggregate functions.
  • `CASE` for buckets, flags, and business logic.
  • Null handling with `COALESCE` or explicit filters.
  • Subqueries or CTEs to keep a query readable.
  • Date filters and date grouping.
  • Window functions if the role touches analytics depth.

Common miss

Candidates often memorize syntax but skip the metric grain. A query can run and still answer the wrong question. In a data analyst interview, saying "one row per customer per month" before you aggregate is a senior signal, even in a basic screen.

Healthcare-claims variation

If the interview mentions healthcare or insurance data, practice with a tiny fake claims table: `claim_id`, `patient_id`, `provider_id`, `service_date`, `diagnosis_code`, `procedure_code`, `claim_status`, `billed_amount`, and `paid_amount`.

  • claims per month by status
  • rejection rate by provider
  • average paid amount by procedure code
  • duplicate claims for the same patient, date, and provider
  • days between service date and paid date
  • top diagnosis codes by paid amount

Practice truthful explanations. Do not claim production healthcare data experience if you only practiced on a small sample.

Run the healthcare claims rep

Move from basic SQL to interview judgment.

The Product Analytics packet adds timed case prompts, worked answers, SQL follow-ups, metric debugging, and recommendation practice for data analyst and product analytics loops.

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Direct purchase note

This is the public $59 self-guided packet path. If a coaching or mock-interview session already gave you access, use that access instead of buying the same packet again.