Salesforce·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

SQL-heavy technical screen for a full-stack role at Salesforce. One main question that branched into a few follow-ups about extending the query and handling edge cases. Pretty standard for this type of round but the follow-ups had some teeth.

Questions Asked (1)

Given a products table, an order_items table, and an orders table, write a SQL query that returns total sales, total cost, profit, and profit ratio for each product. Then walk through how you'd extend it to a per-store, per-month breakdown and how you'd handle products that have zero sales.

Data ModelingProduct Analytics & MetricsTechnical Trade-offs

Author's notes

The base query wasn't bad, I joined order_items to products and did the aggregation with sum(quantity * unit_price) and sum(quantity * unit_cost), derived profit from those two, then divided for the ratio.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and business definitions (e.g., how cost and profit are calculated, what 'sales' means). Then write a base query that joins orders, order_items, and products, aggregates per product, and computes the metrics. Finally, discuss extensions for per-store, per-month breakdowns and handling zero-sales products, emphasizing the use of LEFT JOINs and careful aggregation.

Pro tip: Mention that you would validate the query results against a known sample or use a small dataset to ensure correctness, and that you'd consider indexing strategies for performance on large tables.

1. Clarify requirements and schema

Ask about the table structures, relationships, and definitions of sales, cost, and profit. Confirm whether 'total sales' means revenue (quantity * price) and 'total cost' means quantity * cost per unit.

2. Write base query for per-product metrics

Join orders, order_items, and products, filter by order status if needed, group by product, and compute SUM(quantity * price) as sales, SUM(quantity * cost) as cost, profit as sales - cost, and profit ratio as profit / sales.

3. Extend to per-store, per-month breakdown

Add store_id and date_trunc('month', order_date) to the GROUP BY clause, and include them in the SELECT. Ensure joins still work and consider using a date dimension table if needed.

4. Handle zero-sales products

Use a LEFT JOIN from products to order_items (and orders) so that products with no sales appear with NULL aggregates. Use COALESCE to replace NULLs with 0 for sales, cost, and profit, and handle division by zero for profit ratio.

5. Discuss performance and edge cases

Mention indexing on foreign keys, avoiding unnecessary columns in GROUP BY, and considering materialized views for frequent reporting. Also address potential data issues like returns or discounts.

Key Points to Mention

Use of INNER vs LEFT JOIN and its impact on including zero-sales products
Aggregation functions (SUM, COUNT) and grouping by product, store, and month
Handling NULLs and division by zero with COALESCE and NULLIF
Date truncation for monthly grouping (e.g., DATE_TRUNC('month', order_date))
Performance considerations: indexing, query optimization, and avoiding full table scans
Business logic: defining sales, cost, profit, and profit ratio correctly, and considering order status (e.g., exclude cancelled orders)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.