Data Analytics Interview Guide: Explain and Defend a SQL Portfolio
Prepare a small SQL case study you can explain, reconcile and test during an interview.
What should I demonstrate in an analytics interview?
Show how you turn a business question into a reproducible answer. A polished chart is useful only when you can explain the observation unit, filters and reconciliation behind it. Prepare a small project with a clearly stated question, synthetic or properly permitted data, readable SQL and a short limitations note. Never present practice data as an employer's real revenue.
Build a five-minute case study
Use this invented question: which customer has the highest order value in the sample? State that one row represents one order, amounts use one currency and cancelled orders would need an explicit rule. Explain these assumptions before showing the query. Your interviewer should be able to follow the result without relying on your dashboard.
The following PostgreSQL example was executed locally with PGlite. It is an original exercise rather than evidence of production database experience.
WITH orders(order_id,customer_id,amount) AS (
VALUES (1,10,100),(2,10,50),(3,20,80)
), customers(customer_id,name) AS (
VALUES (10,'Asha'),(20,'Bala')
)
SELECT c.name,SUM(o.amount) AS total
FROM orders o JOIN customers c USING(customer_id)
GROUP BY c.name ORDER BY total DESC,c.name;Expected rows are Asha 150 and Bala 80. Reconcile the grouped total to 230 before talking about visual design. Then explain how you would detect missing customer records and duplicate dimension keys. A join can change the number of result rows; do not assume every matching key appears exactly once.
Rehearse the follow-up discussion
Ask a friend to add a second customer row with customer_id 10. The joined total for that customer's orders now doubles to 300. Your response should be to investigate key uniqueness and the intended relationship, rather than hiding the problem with DISTINCT. Explain what evidence would justify deduplication and which record should survive.
Common preparation mistakes
Avoid memorising syntax without knowing the result. Do not claim that a query is fast simply because three rows finish immediately. If asked about scale, discuss how you would inspect a query plan, examine the actual data and measure an improvement. Be honest about tools you have used and separate local practice from paid project experience.
Practice before the interview
Add an order of 20 for Bala. The overall total becomes 250 and Bala becomes 100. Write down a validation checklist: input count, total amount, missing keys and duplicate keys. Finish with a one-minute explanation of the business decision this tiny dataset cannot support. Bring the SQL, expected output and assumptions together in your portfolio README.
PostgreSQL join documentation.
Prepared by AI Leaf Global Learn for classroom learners in Mudichur/Tambaram, Chennai, and online learners across India. Confirm current batch details with the training team. Tamil Nadu, Chennai and Tambaram are audience tags, not additional branch claims.