📊 DATA ANALYTICS INTERVIEWS
Mock interviews with expert DAs/BAs
Practice SQL, Business case analysis, Excel, Behavioral and Product experimentation with analysts from top companies. Practice real interview questions with structured feedback.
₹12-35L
Salary Range
SQL
Primary Skill
3-5
Interview Rounds
6-8 weeks
Prep Timeline
4 CORE SKILL AREAS
What Companies Test in Data Analyst Interviews
Based on 500+ analyst interview evaluations. Master these 4 areas to pass any DA interview.
SQL & Data Manipulation
Master complex queries, joins, window functions, and CTEs
Foundation - 40% of interviews
Complex JOINs (INNER, LEFT, FULL, CROSS, SELF)
Window functions (ROW_NUMBER, RANK, LAG/LEAD)
CTEs and subqueries for readability
Query optimization and performance tuning
Handling NULLs, duplicates, and edge cases
Business Analysis & Metrics
Translate business questions into data insights
Critical - 30% of interviews
Root cause analysis for metric changes
A/B test design and statistical significance
Funnel analysis and conversion optimization
Cohort analysis and retention metrics
ROI calculation and cost-benefit analysis
Visualization & Dashboards
Design impactful dashboards that drive decisions
Important - 20% of interviews
Chart type selection (bar, line, scatter, heatmap)
Dashboard layout and information hierarchy
Avoiding misleading visualizations
Tableau, Power BI, Looker proficiency
Executive summary design
Communication & Stakeholders
Present complex findings to non-technical audiences
Essential - 10% of interviews
BLUF (Bottom Line Up Front) structure
Translating jargon into business language
Handling questions and pushback
Managing stakeholder expectations
Storytelling with data
SQL CHALLENGE LIBRARY
Common SQL Patterns in Analyst Interviews
These SQL patterns appear in 80% of analyst interviews. Master them to ace the technical round.
Medium
Concept: Subqueries, LIMIT/OFFSET
Find the second highest salary from an Employees table
Solution:
SELECT MAX(salary) as second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
💡 Explanation:
Use subquery to exclude the max, then find the next max
Hard
Concept: Window functions, DATE functions
Calculate 7-day rolling average of daily active users
Solution:
SELECT
date,
COUNT(DISTINCT user_id) as dau,
AVG(COUNT(DISTINCT user_id)) OVER (
ORDER BY date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
) as rolling_7day_avg
FROM user_events
GROUP BY date;
💡 Explanation:
Window function with ROWS BETWEEN for rolling calculations
Hard
Concept: Self-joins, LAG/LEAD, date arithmetic
Find users who made purchases in consecutive months
Solution:
WITH monthly_purchases AS (
SELECT DISTINCT
user_id,
DATE_TRUNC('month', purchase_date) as month
FROM purchases
)
SELECT DISTINCT a.user_id
FROM monthly_purchases a
JOIN monthly_purchases b
ON a.user_id = b.user_id
AND b.month = a.month + INTERVAL '1 month';
💡 Explanation:
Self-join to compare consecutive months for each user
💡 Interview Tip: Always explain your SQL logic out loud. Say: "I'm using LEFT JOIN because we want all users, even those without orders. I'll use COALESCE to handle NULLs as zeros."
INTERVIEW BREAKDOWN
4 Types of Questions You'll Face
Understanding the question mix helps you prepare strategically. Each type requires different skills.
40%
SQL Technical
• Write a query to find top 10 products by revenue
• Calculate month-over-month growth rate
• Identify duplicate records and keep most recent
• Find users who haven't logged in for 30 days
30%
Business Case
• Revenue dropped 15% last week. Investigate.
• Design metrics for a new product launch
• Recommend features to improve retention
• Analyze A/B test results and make recommendation
20%
Dashboard Design
• Design executive dashboard for e-commerce
• Create real-time operations dashboard
• Build customer health scorecard
• Design funnel visualization for conversion
10%
Communication
• Explain p-value to non-technical VP
• Present findings where deadline was missed
• Convince PM their feature idea is failing
• Handle conflicting data between reports
8-WEEK PREP ROADMAP
Your Complete Data Analyst Interview Prep Plan
A structured, week-by-week plan to master SQL, business analysis, and communication.
Week 1-2
SQL Fundamentals
Master JOINs: practice 20+ join problems
Learn window functions (ROW_NUMBER, RANK, LAG/LEAD)
Practice CTEs for query readability
Solve 30+ LeetCode SQL problems (Easy → Medium)
Week 3-4
Advanced SQL & Stats
Master complex window functions and partitioning
Learn query optimization (indexes, execution plans)
Understand statistical significance and p-values
Practice A/B test analysis with SQL
Week 5-6
Business Cases
Practice translating business questions to SQL
Master root cause analysis frameworks
Learn to define and track KPIs
Practice funnel and cohort analysis
Week 7-8
Dashboards & Communication
Design 5+ dashboards for different stakeholders
Practice presenting to non-technical audiences
Learn to handle questions and pushback
Build portfolio of analysis case studies
TOOLS & TECH STACK
Technologies Data Analysts Must Know
Not every role requires all tools, but SQL proficiency is universal. Focus on tools relevant to your target company.
SQL Databases
PostgreSQL
MySQL
Snowflake
BigQuery
Redshift
Visualization
Tableau
Power BI
Looker
Metabase
Google Data Studio
Languages
SQL (primary)
Python (pandas, numpy)
R (optional)
Excel (advanced)
Other Skills
Git version control
Statistical testing
Data modeling
ETL concepts
SUCCESS STORIES
From SQL Practice to Business Impact
These analysts practiced with CrackJobs and landed roles at top companies with measurable impact.
Priya D.
Data Analyst
"Interviewer asked: 'How would you reduce shipping costs?' I wrote SQL to segment orders by route, distance, and carrier. Found 18% of orders were mis-routed. Showed the query, explained the business impact. They loved the practical approach."
Aditya S.
Business Analyst
"CrackJobs taught me to think business-first, not query-first. When Flipkart asked about cart abandonment, I structured my answer: hypothesis → SQL to test → visualization → recommendation."
Kavya R.
Data Analyst
"The case study was brutal—optimize delivery zones. I sketched a dashboard on paper, wrote SQL for distance+demand segmentation, quantified impact. Interviewer said it was the most complete answer they'd seen."
AVOID THESE MISTAKES
5 SQL & Analysis Mistakes That Fail Interviews
Based on 800+ analyst interview evaluations. These mistakes appear in 75% of failed interviews.
Mistake #1
Writing queries without clarifying data assumptions first
Why it fails:
Shows lack of real-world data awareness
✅ How to fix it:
Always ask: Are there duplicates? How are NULLs handled? Date range? Data quality issues? Example: 'Before writing SQL, I'd confirm if user_id is unique or if one user can have multiple rows.'
❌ Common Approach:
-- Assumes user_id is unique
SELECT user_id, COUNT(*) FROM orders;
✅ Better Approach:
-- Handles potential duplicates
SELECT user_id, COUNT(DISTINCT order_id)
FROM orders
GROUP BY user_id;
Mistake #2
Not explaining SQL logic while writing queries
Why it fails:
Interviewers want to see your thought process
✅ How to fix it:
Think out loud: 'I'm using LEFT JOIN here because we want all users, even those without orders. I'll use COALESCE to handle NULLs as zeros.' This shows reasoning, not just syntax.
Mistake #3
Jumping to analysis without exploring data distribution
Why it fails:
Leads to wrong conclusions from outliers or skewed data
✅ How to fix it:
Start with: 'Let me first check data distribution, look for outliers, and segment by key dimensions.' Show methodical thinking: median vs mean, percentiles, date ranges.
Mistake #4
Presenting insights without making them actionable
Why it fails:
Data analysis must drive decisions, not just describe data
✅ How to fix it:
Always end with 'So what?' Example: 'Based on 15% retention drop in Week 1, I recommend we focus on onboarding by implementing email reminders and tutorial improvements. Expected impact: 8-10% retention recovery.'
Mistake #5
Using technical jargon without translating to business impact
Why it fails:
Stakeholders care about outcomes, not statistics
✅ How to fix it:
Don't say: 'p-value is 0.03.' Say: 'We're 97% confident this increase is real, not random. This means we should roll out the feature to all users immediately because it will likely increase revenue by 12%.'
DEEP DIVE GUIDES
Master Specific Analyst Topics
Common SQL Interview Mistakes Data Analysts Make
Learn the most common SQL pitfalls—from query optimization to handling edge cases and NULLs properly.
Read Complete Guide →
HOW IT WORKS
Practice SQL & Analysis in 3 Steps
1
Pick Your Focus
Choose SQL technical, business case, dashboard design, or communication practice. Browse analysts from Amazon, Flipkart, Swiggy.
2
Book 55-Min Session
Practice real SQL problems, business cases, or dashboard reviews. Get live feedback on queries, logic, and communication.
3
Get Actionable Feedback
Detailed evaluation on SQL skills, business thinking, visualization design, and stakeholder communication.
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