Experience: 2-3 years
Agar tum Data Analytics field me ho aur SQL, Power BI ya Tableau ka solid experience hai, toh yeh post tumhare liye ek important opportunity hai. Lagozon Technologies ne Delhi me Data Analyst ke liye Walk-In Drive rakha hai — sirf ek din ka drive hai, 11 July 2026 Saturday ko. Drive 9 AM se 1 PM tak chalegi, toh subah jaldi nikalna hoga.
10 openings hain aur company 100% Data Engineering aur Analytics focused hai — matlab yahan genuinely meaningful data ka kaam milega.
Lagozon Technologies Ke Baare Me
Lagozon ek IT services company hai jo completely Data Engineering, Data Analytics aur AI/ML pe focused hai. Yeh koi generalist IT firm nahi hai — inki poori expertise data space me hai. Consulting se lekar end-to-end data solutions tak, yeh company niche technology areas me kaam karti hai.
Ambitionbox pe 3.8 rating ke saath 58 reviews hain — ek growing mid-size tech company hai jo data professionals ke liye genuinely good environment provide karti hai.
Job Ki Poori Detail
| Detail | Jankari |
|---|---|
| Position | Data Analyst |
| Company | Lagozon Technologies |
| Location | Delhi NCR |
| Drive Date | 11 July 2026 (Saturday) |
| Timing | 9:00 AM – 1:00 PM |
| Experience | 2 – 4 Saal |
| Openings | 10 Posts |
| Job Type | Full Time, Permanent |
| Salary | Not Disclosed |
| Contact | Divya Mehta |
Walk-In Ka Address
B-1 Extension, FB, NH-19, Block B-1, Block E, Mohan Cooperative Industrial Estate, Badarpur, New Delhi – 110044
Eligibility – Kaun Apply Kar Sakta Hai?
- Any Graduate — kisi bhi stream se
- 2 se 4 saal ka Data Analytics ka relevant experience zaroori hai
- SQL — strong command honi chahiye, yeh primary requirement hai
- Power BI ya Tableau — data visualization me experience hona chahiye
- Python — knowledge ho toh plus point hai
- Data Analysis aur Data Visualization me hands-on experience hona chahiye
Yeh role freshers ke liye nahi hai — 2 saal ka experience minimum condition hai.
Kaam Kya Karna Hoga?
Lagozon ek pure-play data company hai, toh kaam bhi genuinely data-heavy hoga:
- Business data collect, clean aur analyze karna hoga
- SQL queries likh ke databases se meaningful insights nikalni hongi
- Power BI ya Tableau me dashboards aur reports banana hoga
- Business stakeholders ke liye data ko clear aur actionable format me present karna hoga
- Data quality ensure karna aur data pipelines ko monitor karna hoga
- Business problems ko data ke through solve karne me help karna hoga
Yeh Job Kyun Karni Chahiye?
Pure Data Focus: Lagozon sirf Data aur AI/ML me kaam karti hai — yahan kaam karna matlab apni analytics skills genuinely sharpen karna, koi generic IT support nahi.
Career Growth: Data Analytics me 2-4 saal ke baad Lagozon jaisi specialized firm me aana ek strong career move hai — Senior Analyst, Data Engineer ya Analytics Lead roles tak pohunchne ka clear path hota hai.
Permanent Role: Contractual nahi — Full Time Permanent job hai.
Apply Karne Ke 2 Tarike Hain
Option 1 – Pehle Online Form Bharo (Recommended):
👉 Click Here – Lagozon Data Analyst Application Form
Drive se pehle form fill karna recommended hai — isse process fast hoti hai.
Walk-In Me Kya Lekar Jaana Hai?
Company ne clearly mention kiya hai — soft copies mandatory hain:
✅ Aadhar Card — soft copy phone me ready rakho
✅ Resume — soft copy bhi aur ek printed copy bhi saath rakho
Interview Ki Taiyari – Yeh Questions Zaroor Padho
Lagozon ka interview SQL aur data analytics skills pe focused hoga. Niche important questions diye hain jo is type ke interviews me aksar pooche jaate hain.
SQL Questions
Q. What is the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN?
INNER JOIN returns only the rows where there is a matching value in both tables. LEFT JOIN returns all rows from the left table and the matching rows from the right table — where there is no match on the right, it returns NULL. FULL OUTER JOIN returns all rows from both tables, with NULLs wherever there is no match on either side.
Q. What are window functions? Give examples.
Window functions perform calculations across a set of rows related to the current row without collapsing the result into a single row like GROUP BY does. Common examples are ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), and SUM() OVER(). For example, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) assigns a rank to each employee within their department based on salary.
Q. What is the difference between WHERE and HAVING?
WHERE filters rows before any grouping or aggregation happens. HAVING filters groups after the GROUP BY operation is applied. For example, to find departments with more than 5 employees, you use HAVING COUNT(*) > 5 — not WHERE — because you are filtering on an aggregated value.
Q. How do you find the second highest salary from a table?
One clean way is using a subquery — SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees). Another approach uses DENSE_RANK() — SELECT salary FROM (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk FROM employees) WHERE rnk = 2.
Q. What is the difference between RANK() and DENSE_RANK()?
Both assign ranks to rows based on a specified order. RANK() skips numbers after a tie — if two rows share rank 2, the next rank is 4. DENSE_RANK() does not skip — if two rows share rank 2, the next rank is still 3. DENSE_RANK() is more useful when you need continuous ranking without gaps.
Q. What is a CTE and when do you use it?
CTE stands for Common Table Expression. It is a temporary named result set defined using the WITH clause that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. CTEs make complex queries more readable and organized — especially when the same subquery needs to be referenced multiple times or when logic needs to be broken into clear steps.
Q. What is the difference between DELETE, TRUNCATE, and DROP?
DELETE removes specific rows from a table based on a WHERE condition — it can be rolled back. TRUNCATE removes all rows from a table at once and is faster than DELETE — it cannot be rolled back in most databases. DROP removes the entire table structure along with all its data permanently.
Power BI & Tableau Questions
Q. What is the difference between Power BI and Tableau?
Both are leading data visualization tools. Power BI integrates tightly with the Microsoft ecosystem — Excel, Azure, SharePoint — and is more cost-effective for organizations already using Microsoft products. Tableau is known for its superior visualization capabilities and handles very large, complex datasets more smoothly. Both have strong communities and are widely used in industry.
Q. What are measures and dimensions in Power BI or Tableau?
Dimensions are categorical fields used to slice and group data — like product name, region, or date. Measures are numerical values that are aggregated — like sales, profit, or count of orders. In Power BI, measures are typically written using DAX formulas. In Tableau, they appear as green pills (continuous) versus blue pills (discrete).
Q. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It is the formula language used in Power BI to create calculated columns, measures, and tables. DAX functions like CALCULATE, SUMX, FILTER, and RELATED allow you to build complex business logic directly in your Power BI model — similar to writing advanced Excel formulas but much more powerful.
Q. What is a calculated field in Tableau?
A calculated field lets you create new data from existing fields using Tableau’s formula syntax. For example, you can create a profit margin field by dividing profit by sales, or use IF/THEN logic to create a category-based field. Calculated fields allow you to add business logic to your visualizations without modifying the original data source.
Q. How do you optimize a slow Power BI dashboard?
Reduce the number of visuals on a single page, avoid using too many card visuals with complex measures, use aggregated tables instead of row-level data where possible, minimize the use of bidirectional relationships in the data model, use Query Folding in Power Query, and use DAX variables to avoid redundant calculations. Also, setting appropriate data types and removing unnecessary columns from the data model improves performance significantly.
Python & Data Analysis Questions
Q. What is the difference between a list and a dictionary in Python?
A list is an ordered collection of items accessed by index. A dictionary is an unordered collection of key-value pairs accessed by key. In data analysis, dictionaries are often used to map labels or create lookup tables, while lists are used for sequential data.
Q. How do you handle missing values in a Pandas DataFrame?
Check for missing values using df.isnull().sum(). Then decide the strategy — drop rows or columns with df.dropna(), fill missing values with a specific number using df.fillna(value), or fill with the column mean or median using df.fillna(df[‘column’].mean()). The choice depends on how much data is missing and the context of the analysis.
Q. What is the difference between groupby() and pivot_table() in Pandas?
groupby() splits data into groups based on one or more columns and applies an aggregation function. pivot_table() does the same but returns the result in a spreadsheet-style layout with rows and columns — making it easier to read multi-dimensional summaries. pivot_table() also handles missing values more gracefully with the fill_value parameter.
Data Analytics Conceptual Questions
Q. What is the difference between descriptive, diagnostic, predictive, and prescriptive analytics?
Descriptive analytics answers “what happened” — summarizing historical data through reports and dashboards. Diagnostic analytics answers “why did it happen” — identifying patterns and root causes. Predictive analytics answers “what will happen” — using statistical models and machine learning to forecast future outcomes. Prescriptive analytics answers “what should we do” — recommending actions based on predictive insights.
Q. What is data cleaning and why is it important?
Data cleaning is the process of identifying and correcting errors, inconsistencies, and missing values in a dataset before analysis. Raw data almost always has issues — duplicates, incorrect formats, outliers, null values — and if these are not addressed, the analysis built on top will be flawed. Garbage in, garbage out — clean data is the foundation of reliable insights.
Q. What is an outlier and how do you handle it?
An outlier is a data point that is significantly different from the rest of the dataset. It can be detected using statistical methods like Z-score or IQR (Interquartile Range). Handling depends on the context — if the outlier is due to a data entry error, it should be corrected or removed. If it is a genuine extreme value, it may need to be kept and noted. In some cases, outlier-robust statistical methods are used instead of removing data.
Q. Walk me through a data analysis project you have worked on.
Structure your answer clearly — what was the business problem, what data did you work with, what tools and techniques did you use (SQL, Python, Power BI/Tableau), what insights did you find, and what was the business impact. Quantify the impact where possible — “this analysis helped reduce churn by 15%” is far stronger than a vague description.
HR Questions
Q. Tell me about yourself.
Mention your degree, your years of experience in data analytics, the tools you work with — SQL, Power BI, Tableau, Python — and one or two meaningful projects you have worked on. Keep it to 2-3 minutes and stay focused on what is relevant to this role.
Q. Why do you want to join Lagozon?
Say that Lagozon’s 100% focus on Data Engineering and Analytics aligns perfectly with where you want to grow your career. Working in a company where data is the core business — not a support function — means deeper exposure, better projects, and faster growth as a data professional.
Q. What is your biggest strength as a Data Analyst?
Pick one genuine strength — maybe it is your SQL skills, your ability to translate complex data into simple stories for business stakeholders, or your attention to data quality. Give a brief example that backs it up.
Last Minute Tips
✅ Drive sirf ek din ki hai — 11 July, Saturday. Subah 9 baje se 1 baje tak. Time pe pahuncho — preferably 9 se 9:30 ke beech
✅ Aadhar aur Resume ki soft copies phone me ready rakho — yeh mandatory hai company ne clearly mention kiya hai
✅ SQL window functions ek baar practice karo — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
✅ Apna ek strongest analytics project 5 minute me clearly explain karne ki practice karo — numbers aur impact ke saath
✅ Power BI ya Tableau me koi dashboard banaya ho toh uska screenshot ya link ready rakho dikhane ke liye
✅ Divya Mehta se baat karni ho toh:
Aakhri Baat
Lagozon ek pure data company hai — yahan kaam karna matlab apni SQL, Power BI aur analytics skills genuinely next level pe le jaana. 10 openings hain aur drive sirf ek din ki hai — kal subah ready ho jao.
👉 Pehle Yeh Form Fill Karo – Lagozon Data Analyst Application
📍 Mohan Cooperative Industrial Estate, Badarpur, New Delhi – 110044 📅 11 July 2026 (Saturday) | ⏰ 9 AM – 1 PM 👤 Contact: Divya Mehta
Yeh post apne un connections ke saath share karo jo Data Analytics me 2-4 saal ka experience rakhte hain — 10 openings hain aur yeh genuinely strong opportunity hai. Aur aisi aur job vacancies ke liye humara blog bookmark karke rakho.