CITI IS HIRING : DATA ANALYTICS

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interview questions and detailed answers for the Data Analytics Analyst

1. What is Oracle PL/SQL and how is it used in data analytics?

PL/SQL stands for Procedural Language extensions to SQL. It is Oracle’s programming language used to write procedures, functions, and scripts that can handle complex operations inside the Oracle database.

In data analytics, PL/SQL helps in:

• Writing queries to extract data from large tables

• Performing data cleaning and transformation

• Creating automated ETL scripts

• Improving performance by using procedures instead of running multiple queries

Example: You can write a PL/SQL procedure to get the monthly sales data of all products, clean null values, and store the output in a new table.

2. How do you use Python for data analysis and automation?

Python is a powerful programming language for handling, processing, and analyzing data. It offers many libraries like:

• Pandas for working with structured data (like Excel or CSV files)

• NumPy for numerical calculations

• Matplotlib and Seaborn for visualizing data

• openpyxl or xlrd for Excel automation

Python is also used to automate repetitive tasks, such as reading files, cleaning data, generating reports, or sending emails.

Example: You can write a Python script that connects to a database, runs a query, processes the data with Pandas, and then sends a daily report as an email.

3. What is ETL, and what is your experience with ETL processes?

ETL stands for Extract, Transform, Load. It is a method of collecting data from different sources, transforming it into a useful format, and then loading it into a database or data warehouse.

In this role, ETL processes might involve:

• Extracting customer data from a database using PL/SQL

• Transforming it using Python (e.g., removing duplicates, renaming columns)

• Loading it into a reporting system like Power BI

ETL helps maintain clean, organized, and reliable data for decision-making.

4. Have you worked with data visualization tools like Tableau or Power BI? What did you do?

Yes. Tools like Tableau and Power BI are used to create visual dashboards and reports. These tools allow you to show patterns, trends, and summaries in an easy-to-understand way.

Example: You can use Tableau to create a dashboard that shows customer behavior by region, age group, or purchase pattern using charts and graphs. You can also add filters for real-time interaction.

In a real project, I used Power BI to visualize monthly sales trends and compare performance across regions using bar graphs and pie charts.

5. What do you understand by AI/ML in data analytics? Can you give an example?

AI (Artificial Intelligence) and ML (Machine Learning) are used in data analytics to make predictions or automate decision-making.

In this role, you may:

• Use ML to predict customer churn

• Apply NLP to analyze customer feedback

• Use AI tools to detect fraud patterns

Example: Using a decision tree model in Python, you can predict whether a bank customer will default on a loan based on historical data like income, age, and past credit history.

 6. What steps do you follow when analyzing a new dataset?

When working with a new dataset, the steps are:

1. Understand the business requirement – know what the goal is.

2. Explore the data – use Pandas to check rows, columns, data types, missing values.

3. Clean the data – handle nulls, remove duplicates, correct data formats.

4. Analyze the data – find trends, group data, calculate averages or totals.

5. Visualize the results – use Power BI, Tableau, or Matplotlib.

6. Share insights – write a report or create a dashboard.

7. What challenges did you face while working with large datasets? How did you handle them?

Working with large datasets can lead to:

• Performance issues (slow queries)

• Memory errors in Python

• Difficulty in joining multiple large tables

To solve these:

• I used indexing in PL/SQL to improve query speed.

• Used Python’s chunking feature to read large files in smaller parts.

• Optimized SQL queries with proper joins and where clauses.

8. How do you ensure data quality and accuracy?

To ensure data quality:

• I check for missing values and null entries

• I remove duplicate records

• I apply data validation rules (e.g., date format, value ranges)

• I compare summary results with known values to ensure correctness

Example: Before sending a report, I check whether the total number of customers matches the number in the source database.

9. Explain a project where you used both SQL and Python.

In one project, I had to analyze customer spending behavior:

• I used PL/SQL to extract raw transaction data from Oracle DB.

• Then, I used Python (Pandas) to clean and group the data.

• I generated insights like “Top 10 customers by total spend” and “most purchased products”

• Finally, I created a Power BI dashboard to present the results.

This combination allowed faster extraction and detailed analysis.

10. Why do you want to work at Citi in this Data Analytics Analyst role?

Citi is a globally respected financial institution with a strong focus on innovation and data-driven decision-making. I’m particularly interested in this role because it combines technical skills (PL/SQL, Python) with strategic data analysis responsibilities.

I also appreciate the opportunity to explore AI/ML techniques and work on real business problems. I believe this role offers a great environment to grow technically and contribute meaningfully to business outcomes.

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