Data Analyst Interview FAQs: Most Asked Questions in 2026

Data Analyst Interviews · FAQ 2026

Data Analyst Interview FAQs: Most Asked Questions in 2026

SECTION 01SQL and data questions

SQL is the most heavily tested area in data analyst interviews. Expect questions on joins, aggregations, window functions, and data-cleaning logic.

-- Second-highest salary using a subquery
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

-- Or using DENSE_RANK (safer with ties)
SELECT salary
FROM (
  SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk
  FROM employees
) ranked
WHERE rnk = 2;

-- Interviewers often ask follow-ups:
-- What if there are ties?
-- What if there is only one salary?

-- Find duplicate emails in a users table
SELECT email, COUNT(*) AS count
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

-- Find duplicate rows using ROW_NUMBER
SELECT *
FROM (
  SELECT *,
         ROW_NUMBER() OVER (PARTITION BY email ORDER BY id) AS rn
  FROM users
) ranked
WHERE rn > 1;

-- Interviewers check:
-- Do you know GROUP BY + HAVING?
-- Do you know window functions?
-- Do you handle case-sensitivity / trimming?

SECTION 02Statistics questions

Statistics questions test whether you can interpret data, not just compute numbers. Here are the most common ones.

SECTION 03Excel and Python questions

Excel and Python questions test practical data manipulation. Most companies expect at least one of these to be strong.

# Group and aggregate sales data with Pandas
import pandas as pd

df = pd.read_csv("sales.csv")

# Total sales per region
region_totals = df.groupby("region")["amount"].sum()
print(region_totals)

# Multiple aggregations in one step
summary = df.groupby("region").agg(
    total_sales=("amount", "sum"),
    avg_sales=("amount", "mean"),
    orders=("order_id", "count")
)
print(summary)

# Interviewers often ask: "How would you sort and take top 3?"
print(summary.sort_values("total_sales", ascending=False).head(3))

# Handling missing values — common interview topic
import pandas as pd
import numpy as np

df = pd.read_csv("data.csv")

# Check how much is missing
print(df.isnull().sum())

# Option 1: Drop rows with too many missing values
df_clean = df.dropna(thresh=len(df.columns) * 0.8)

# Option 2: Fill numeric missing values with median
df["amount"] = df["amount"].fillna(df["amount"].median())

# Option 3: Fill categorical with a placeholder
df["city"] = df["city"].fillna("Unknown")

# Interviewers look for:
# - Awareness of trade-offs
# - Knowing when to drop vs fill
# - Not blindly filling with 0

SECTION 04Case study questions

Case studies are where business thinking is tested. Interviewers want to see how you frame problems, choose metrics, and draw conclusions.

SECTION 05Behavioural and preparation tips

Behavioural questions test communication and collaboration. Data analysts work with teams, so interviewers look for clear storytelling and stakeholder awareness.

• Use STAR — Situation, Task, Action, Result. Keep stories short and specific.
• Explain decisions — when describing a project, highlight why you chose the metrics and methods.
• Show ownership — highlight where you pushed back on incorrect assumptions or clarified a vague request.
• Prepare 3-4 project stories — covering analysis, cleaning, and stakeholder communication.
• Practice aloud — talk through your SQL and Python logic before the interview.

SECTION 06Interview Q&A — data analyst prep

SECTION 07Test yourself — data analyst interview quiz

SECTION 08Frequently asked questions

SECTION 09Related reads