Tesla·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Tesla data engineering interview, one technical SQL question that looks straightforward until you actually sit down to write it. The monthly reset part is where things get interesting.

Questions Asked (1)

Given a table with columns for date, store ID, and sale amount, write a SQL query that calculates cumulative sales per store, but the running total resets at the beginning of each month. Return store ID, date, and the cumulative amount.

Data ModelingAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The reset-per-month part tripped me up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and expected output, then explain that you'll use a window function with PARTITION BY store_id and the year-month extracted from the date, ordering by date to compute the running total. Write the query using SUM(sale_amount) OVER (PARTITION BY store_id, EXTRACT(YEAR FROM date), EXTRACT(MONTH FROM date) ORDER BY date) and discuss how this resets monthly.

Pro tip: Mention that you'd verify the query handles ties in dates (e.g., multiple sales on the same day) by adding a secondary ordering column like sale_id or using ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW to ensure deterministic results.

1. Clarify requirements and schema

Ask about the table name, column names, data types, and whether there can be multiple rows per store per date. Confirm the expected output format and whether the cumulative sum should include the current row's sale.

2. Identify the reset condition

Recognize that the running total resets at the start of each month, so the partition must include both store ID and the year-month derived from the date column.

3. Choose the window function approach

Use SUM(sale_amount) as a window function with PARTITION BY store_id and date_trunc('month', date) (or equivalent) and ORDER BY date. Explain that this computes a cumulative sum within each store-month group.

4. Write and explain the SQL query

Construct the query, selecting store_id, date, and the cumulative sum. Optionally, use a subquery or CTE to first extract the month, then apply the window function for clarity.

5. Discuss edge cases and optimizations

Address handling of ties, nulls, and performance considerations such as indexing on (store_id, date) and avoiding unnecessary sorting.

Key Points to Mention

Use of window functions (SUM OVER) with PARTITION BY and ORDER BY.
Partitioning by store_id and the month extracted from the date to reset cumulative sum monthly.
Handling of ties in date ordering by adding a tiebreaker column (e.g., sale_id) or using ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
Consideration of database-specific syntax (e.g., DATE_TRUNC in PostgreSQL, EXTRACT in MySQL, FORMAT in SQL Server).
Performance implications: indexing on (store_id, date) to speed up window function operations.
Clarifying whether the cumulative sum should include the current row (default) or not.

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