← OneMain Financial Interview Insights
Start by clarifying the table schema and the definition of an order, then write a SQL query that groups by customer and counts orders. Use a LEFT JOIN from customers to orders to include customers with zero orders, and explain how you'd handle edge cases like cancelled orders or duplicates.
Pro tip: Mention that you'd validate the query by checking for customers with zero orders and comparing the total count to the raw orders table. This shows attention to data quality and business context.
Ask about the tables involved (e.g., customers, orders), the definition of an order (e.g., status, date range), and whether to include customers with zero orders.
Decide between INNER JOIN (only customers with orders) and LEFT JOIN (all customers). Use COUNT(orders.id) to avoid counting NULLs, and GROUP BY customer_id.
Construct the query, selecting customer_id and COUNT(orders.id) AS order_count, joining customers to orders, and grouping by customer_id.
Consider filtering by order status or date, handling duplicates, and validating results by checking totals or spot-checking specific customers.
Describe how the output would be used (e.g., for customer segmentation) and mention any follow-up analyses like average orders per customer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the definition of revenue and the grain of the data (e.g., transaction-level vs. aggregated). Then write a SQL query that groups by date and sums the revenue column, ensuring you handle date truncation and time zones appropriately.
Pro tip: Always confirm whether revenue should be net of refunds or discounts, and whether you need to include only completed transactions. This shows business acumen and prevents incorrect results.
Ask about the definition of revenue (gross vs. net), the date field to use (transaction date, order date), and any filters (e.g., exclude refunds, only completed orders).
Determine which table contains revenue data and the date column. Confirm the grain of the table (one row per transaction or per order).
Use a GROUP BY on the date (truncated to day) and SUM the revenue column. Apply any necessary filters in a WHERE clause.
Check for NULLs, time zone conversions, and date ranges. Consider using COALESCE for NULL revenue and ensure dates are in the correct format.
Walk through the query, explain assumptions, and mention any potential pitfalls (e.g., duplicate rows, currency conversion).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the schema and define 'single order amount' as the total value of an order (e.g., sum of line items). Then write a SQL query that computes each order's total, finds the maximum, and returns all customers whose order total equals that maximum, handling ties appropriately.
Pro tip: Mention that you would validate the result by checking for ties and considering whether to return all tied customers or just one, and discuss performance implications of different approaches (e.g., window functions vs. subqueries).
Identify the relevant tables (e.g., customers, orders, order_items) and how they relate. Determine how to compute the total amount for a single order.
Write a subquery or CTE that calculates the total amount for each order by summing line item amounts or using a precomputed order total column.
Use MAX() on the computed order totals to get the highest single order amount.
Join the order totals back to customers and filter for orders where the total equals the maximum. Use a window function like RANK() or DENSE_RANK() to handle ties efficiently.
Decide whether to return all customers with the highest order amount or just one. Validate the result by checking for multiple orders with the same maximum and ensuring no duplicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the table schema and the definition of 'amount' and 'order'. Then write a SQL query that computes the overall average order amount using a subquery or window function, and filters orders where the amount exceeds that average. Consider edge cases like NULLs, ties, and whether to include all orders or only completed ones.
Pro tip: Mention that using a window function (AVG() OVER ()) avoids a self-join and is more efficient for large datasets, but a subquery is more portable across databases. Also, discuss how you would handle ties or if the average should be recalculated dynamically.
Ask about the table structure, column names, and any filters (e.g., only completed orders). Confirm whether 'overall average' means across all orders or a subset.
Decide between a subquery (SELECT AVG(amount) FROM orders) or a window function (AVG(amount) OVER ()). Consider performance and portability.
Use a WHERE clause to select orders where amount > (subquery) or use a CTE with window function and filter in outer query.
Address NULL amounts, ties (orders equal to average), and whether to include all orders or only those with non-null amounts.
Check results for correctness, consider indexing on amount, and discuss potential performance improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.