Scenario

 Tell me about how you ensure data quality:

Conducting data profiling, data mapping, and root cause analysis to ensure data accuracy and integrity.

In one of my projects, I was responsible for ensuring data accuracy across multiple systems. I started with data profiling using SQL and Excel to identify nulls, outliers, and data type mismatches. Then, I performed data mapping between source and target systems to ensure the data transformation logic aligned with business rules. When discrepancies were found, I conducted root cause analysis—this often involved tracing issues back to source systems or flawed ETL logic. Based on findings, I collaborated with engineering and business teams to fix the issues and implement validation rules. As a result, we significantly reduced errors in downstream reports.

Tell me how you improved a data process

Optimize data pipelines and ETL processes to improve the efficiency

In a recent project, I was tasked with optimizing our ETL pipeline, which was taking hours to complete and often failed during peak loads. I started by analyzing each stage using SQL execution plans and Python logging. I optimized complex joins, added indexes in the source database, and rewrote inefficient transformation scripts using vectorized pandas operations. I also added incremental loading instead of full refreshes. These improvements cut processing time by 40% and significantly reduced failures, which helped the analytics team access updated data faster.

🛠️ Project Summary:

Title: ETL Pipeline Optimization for Banking Analytics
Scope:

• Assessed performance of existing ETL jobs handling millions of transactions daily

• Refactored SQL scripts and used partitioning, indexing, and query tuning to speed up transformations

• Rewrote Python-based transformation logic for efficiency using pandas and joblib (parallel processing)

• Implemented incremental loads using timestamps and CDC (Change Data Capture) Impact:

• Reduced ETL runtime by 40%

• Improved system reliability and enabled near real-time reporting

• Minimized infrastructure costs through better resource utilization

How did you optimize SQL queries?

In a recent project, I optimized several slow-running SQL queries that were affecting dashboard performance. I started by analyzing execution plans to identify bottlenecks like full table scans and missing indexes.

I optimized the queries by:

• Replacing subqueries with JOINs and CTEs for better readability and efficiency

• Creating indexes on frequently filtered and joined columns

• Avoiding SELECT * and retrieving only necessary columns

• Rewriting complex logic into temporary tables or staging queries for modularity

As a result, I reduced query execution time by over 60%, which significantly improved data refresh rates and end-user experience. I always validate performance improvements using query statistics and test on production-sized datasets.

 

How did you increase the performance of SQL queries?”

In one of my projects, I was responsible for improving the performance of SQL queries used in business-critical reports. Some queries were running slowly, especially when dealing with large transactional tables.

I started by using SQL execution plans and monitoring tools to identify performance bottlenecks. I noticed that a few queries lacked proper indexes and were doing full table scans.

To improve performance, I:

• Added indexes to commonly filtered and joined columns

• Rewrote subqueries as JOINs or CTEs to reduce nesting and improve readability

• Replaced SELECT * with specific columns to minimize data transfer

• Used temporary tables for complex calculations and intermediate results

• Applied query hints and partitioning strategies where appropriate

After optimization, most queries saw a performance boost of 50–70%, which greatly reduced report load times and improved system stability during peak usage hours. 

“How do you present insights and findings to senior management?”

When presenting insights to senior management, I focus on being clear, concise, and business-oriented. I always start by understanding what matters most to the stakeholders—whether it’s revenue trends, customer churn, or operational efficiency.

Once I’ve completed my analysis, I summarize the key takeaways using visuals like charts, KPIs, and trend lines, often through Power BI dashboards or slide decks. I avoid technical jargon and translate findings into actionable business language—for example, instead of saying "a 20% drop in conversions", I’d say "we’re potentially losing X amount in monthly revenue."

I also make sure to:

• Highlight why the insight matters

• Offer recommendations backed by data

• Prepare for follow-up questions with deeper drill-downs or supporting data

One example: I identified a spike in customer churn from one region. I presented it using a simple line chart, tied it to recent service changes, and recommended customer outreach strategies. Leadership acted on it, and churn dropped the following quarter.

What were the insights you found?

In one of my recent projects, I analyzed customer transaction data and discovered that:

1. Customer churn was highest among users who didn’t engage within the first 7 days of onboarding.
   👉 This insight led to a redesign of our welcome process and automated early engagement emails.

2. Loan approval times were delayed primarily due to manual verification steps.
   👉 We recommended automation for certain checks, which reduced processing time by 30%.

3. Cross-sell opportunities were being missed—customers who purchased product A had a 40% higher chance of buying product B, but there was no campaign targeting them.
   👉 We proposed a targeted upsell strategy which marketing implemented.

These insights were shared in a Power BI dashboard, using visuals and filters that allowed management to drill into specific customer segments and regions.

Goal of the Dashboard or Analysis:

• What was the primary purpose of the dashboard or analysis? For example, were you tracking sales performance, customer behavior,, or operations?

Data Sources:

• What kind of data were you analyzing? (e.g., structured data from SQL databases, unstructured data like customer feedback, API data, etc.)

Audience:

• Who was the target audience for the insights? (e.g., senior management, finance team, marketing team)

Key Insights:

• What were the most significant findings from your analysis? For example, did you discover patterns in customer behavior, operational bottlenecks, or trends in sales?

Action Taken:

• Were any decisions or actions made based on your insights? For example, did you recommend changes to marketing strategies, process improvements, or product offerings?

Scenario:

You’ve joined the Business Insights team at a mid-sized e-commerce company. Over the last quarter, overall revenue has been flat but when broken down by region, the West has seen a significant drop. Leadership wants answers before the next strategy meeting.

 

You're given access to:

 

• Transactions table (date, product_id, customer_id, quantity, revenue, region)
• Product catalog (product_id, category, price)
• Marketing campaigns (campaign_id, dates, region, spend)
• Customer database (customer_id, location, signup_date)
   

What the Interviewer is Assessing:
 

1. SQL/Data Extraction
   
   • Can you write efficient queries to pull relevant trends?
   • Can you join and filter datasets accurately?
      
2. Exploratory Analysis
   
   • How do you break down the problem?
   • What metrics do you prioritize (AOV, new vs returning users, category-level drop, etc.)?
      
3. Visualization & Communication
   
   • Can you create clean, focused charts (Excel, Tableau, or Power BI)?
   • Can you tell a clear story with the data?
      
4. Business Thinking
   
   • Do you tie insights to actionable business decisions?
   • Can you distinguish correlation vs causation?
      

What a Good Answer Includes:
 

• Segment sales by region, product category, and customer type.
• Identify if the drop is in quantity sold or AOV.
• Correlate drops with marketing spend—was there a campaign cut?
• Check if West region saw a decline in new user acquisition.
• Summarize key takeaways and propose a plan (e.g., targeted campaigns, bundling high-return products, A/B testing new pricing).