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REQUIREMENT_ID_190 β€’ 3-DAY_ACTIVE_POLICY

Data Analyst

Company
Company Navi Technologies
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 6-8 LPA
Location
Location India
Posted Date
Posted Date Yesterday
Excel SQL Tableau Power BI ETL Python R VLOOKUP Pivot Tables data wrangling problem solving attention to detail
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Data Analysis Fundamentals Open Resource β†—
Essential concepts for mastering Excel, SQL, and data visualization tools used in analytics roles.
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Interview Preparation Guide Open Resource β†—
Comprehensive resource for understanding common interview questions and best practices for technical and behavioral rounds.
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Career Pathways in Analytics Open Resource β†—
Insights into the progression and skill requirements for data analyst roles across industries.
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Coding Challenges for Data Professionals Open Resource β†—
Practice problems to sharpen analytical thinking and coding skills relevant to data roles.

Bachelor’s degree in Technology (any branch). 1–3 years of experience as a data analyst or related role. Strong proficiency in Excel, SQL, and a data visualization tool (Tableau or Power BI). Basic understanding of ETL processes. Ability to interpret complex data sets and strong problem‑solving skills. Prior experience with Python or R is a plus. No specific percentage requirement; however, a strong academic record and relevant project experience are preferred.

1
Round 1: Technical interview focusing on SQL, Excel, and Tableau skills
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Round 2: HR/Behavioral interview to assess cultural fit
3
Round 3: Managerial interview with senior analytics lead
Navi is a fast‑growing fintech company that aims to make financial services accessible to a billion Indians. Founded in 2018 by Sachin Bansal and Ankit Agarwal, the company offers a wide range of products including personal and home loans, UPI, insurance, mutual funds, and gold. With a strong customer‑first approach and a tech‑first mindset, Navi has rapidly scaled its operations across India, building a robust digital ecosystem that powers millions of users. The Analytics team at Navi is pivotal in turning data into actionable insights. As a Data Analyst in the central analytics team, you will act as a bridge between business stakeholders and data engineers. Your primary responsibility will be to create visibility across key business KPIs by building dashboards, reports, and automated data pipelines. You will work closely with product, credit, collections, and automation teams to validate hypotheses, uncover trends, and recommend data‑driven solutions that directly impact business outcomes. Key responsibilities include: 1. Collecting, cleaning, and maintaining large datasets from multiple sources. 2. Designing and developing interactive dashboards in Tableau or Power BI. 3. Writing complex SQL queries and using Excel functions (VLOOKUP, Pivot Tables) for ad‑hoc analysis. 4. Implementing data quality checks and ensuring data accuracy and consistency. 5. Automating repetitive reporting tasks to improve efficiency. 6. Collaborating with cross‑functional teams to streamline data collection workflows. 7. Identifying bottlenecks in existing processes and proposing enhancements. 8. Presenting insights and recommendations to senior management. 9. Supporting data modeling initiatives for a scalable data infrastructure. 10. Staying updated with industry best practices in analytics and data engineering. Tech stack: Excel, SQL, Tableau/Power BI, ETL tools, basic Python or R for data wrangling. Growth path: Starting as a junior analyst, you can progress to senior analyst, analytics lead, or data engineering roles. Navi’s culture of ownership and continuous learning provides ample opportunities for skill development and career advancement. Why join Navi? The company’s mission to democratize finance aligns with a purpose‑driven career. You’ll work in a collaborative environment that values innovation, ownership, and customer impact. The fast‑paced fintech space offers exposure to cutting‑edge technologies and real‑world business challenges, making it an ideal launchpad for aspiring data professionals.

Navi Technologies 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 🎯
Navi Technologies Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Navi Technologies 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 (Excel, SQL, Tableau, Power BI, ETL, Python, R, VLOOKUP, Pivot Tables, data wrangling, problem solving, attention to detail) & 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
Navi Technologies 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 Navi Technologies as a Data Analyst?
Preparation Tip: Highlight Navi Technologies's market reputation, recent tech innovations, and how your skills in Excel 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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