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REQUIREMENT_ID_1403 β€’ 3-DAY_ACTIVE_POLICY

Data Analyst

Company
Company Headout
Type
Opportunity Type Internship
Salary
Stipend / Salary 6 LPA - 9 LPA
Location
Location Bengaluru
Posted Date
Posted Date Sep 28, 2026
SQL Python Data Visualization BI Tools (Metabase Looker Tableau Power BI) Analytical Thinking Problem Solving Communication Basic Data Warehousing dbt Airflow
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Aptitude Questions and Answers Open Resource β†—
Covers quantitative and logical reasoning problems that help sharpen the analytical thinking required for the screening test.
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Data Analyst Interview Preparation Open Resource β†—
Provides curated articles and practice problems to reinforce SQL, Python and BI concepts relevant to Headout's interview.
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Headout Data Analyst Prep Guide Open Resource β†—
Specific guidance on the role, including sample questions, case studies and tips to align your answers with Headout's business model.
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Algorithm Practice Problems Open Resource β†—
A collection of coding challenges to improve problem‑solving speed and accuracy, useful for the technical assessment.

Graduates (B.Tech/B.E., B.Sc., B.Com, BBA or equivalent) from any stream with a minimum of 60% aggregate (or CGPA 6.5/10). Must have completed the 2025 batch (or graduating in 2024‑2026). No active backlogs at the time of joining. Strong analytical mindset, proficiency in SQL and Python, and exposure to BI tools are mandatory. Internships or project work in analytics, business intelligence or data science will be considered a plus.

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Round 1: Online assessment – SQL and Python coding test
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Round 2: Technical interview – deep dive into past projects, case studies and problem‑solving
3
Round 3: HR interview – cultural fit, career aspirations and compensation discussion
Headout is a fast‑growing marketplace that connects travellers with real‑world experiences – from immersive tours and museum tickets to live events across more than 700 cities. Backed by over $60β€―million from top‑tier investors, the company has crossed the $200β€―million revenue mark, achieved profitability for 18 consecutive months and continues to scale its operations globally. The culture is built around ownership, speed and impact, encouraging every employee to treat the business as their own startup. With a clear focus on sustainable growth, Headout offers a stable yet dynamic environment where data‑driven decisions shape the product roadmap and market expansion. The Data Analyst role sits at the heart of this ecosystem. Reporting to senior analysts and often collaborating directly with leadership, the analyst will design, build and maintain dashboards, automated reports and analytical models that power decisions across growth, product, operations and finance. The position is deliberately broad – you may spend a week digging into conversion funnels for marketing campaigns, the next week analysing supplier economics, and later help the product team run A/B tests. This exposure ensures you develop a holistic view of a marketplace business while sharpening technical depth. **Key Responsibilities** 1. Design and develop end‑to‑end reporting solutions using SQL and a BI tool (Metabase, Looker, Tableau or Powerβ€―BI). 2. Translate business questions into analytical frameworks, write complex queries with joins, window functions and CTEs, and ensure performance optimisation. 3. Automate recurring data pipelines and dashboards, reducing manual effort and improving data freshness. 4. Conduct ad‑hoc analysis for growth, product, operations and finance teams, delivering actionable insights within tight timelines. 5. Partner with senior analysts to build attribution models, funnel analyses and cohort studies that guide strategic initiatives. 6. Participate in data‑quality audits, identify gaps in source systems and recommend improvements. 7. Document analytical methodology, assumptions and findings for cross‑functional consumption. 8. Mentor junior analysts or interns on SQL best practices and BI tool usage. 9. Stay updated on emerging analytics tools (dbt, Airflow) and propose adoption where beneficial. 10. Contribute to a data‑driven culture by presenting insights in clear, visual formats to stakeholders at all levels. **Tech Stack**: PostgreSQL/MySQL, advanced SQL, Python (pandas, numpy), BI tools (Metabase, Looker, Tableau, Powerβ€―BI), optional exposure to dbt, Airflow, data‑warehouse concepts. **Growth Path**: Successful analysts can progress to Senior Analyst, Analytics Lead, and eventually Head of Data & Insights, with opportunities to move into product management or strategy roles. **Why Join Headout?** The company offers a rare blend of startup agility and corporate stability. You will work on real‑world problems that affect millions of travellers, receive mentorship from seasoned data professionals, and see the direct impact of your work on business outcomes. The steep learning curve, cross‑functional exposure and competitive compensation make it an ideal launchpad for ambitious analysts.

Headout β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
Headout Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Headout and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (SQL, Python, Data Visualization, BI Tools (Metabase, Looker, Tableau, Power BI), Analytical Thinking, Problem Solving, Communication, Basic Data Warehousing, dbt, Airflow) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Headout Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join Headout as a Data Analyst?
Preparation Tip: Highlight Headout's market reputation, recent tech innovations, and how your skills in SQL directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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