Tesla·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Tesla data engineering interview, one SQL question that looks straightforward until you actually sit down and think about what 'one pass' means. Pretty focused technical screen, no fluff.

Questions Asked (1)

Given an orders table with columns order_id, status, amount, and date, write a single SQL query that computes in one pass: total revenue for completed orders per date, count of pending orders per date, and count of cancelled orders per date.

Data ModelingTechnical Trade-offs

Author's notes

The 'one pass' constraint is where it gets interesting.

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AI HintsAI Generated

Suggested Approach

Use conditional aggregation with a single GROUP BY on the date column, applying CASE expressions inside SUM and COUNT to compute each metric. This avoids multiple scans of the table and demonstrates efficient SQL. Write the query clearly, then explain the logic and any assumptions about status values.

Pro tip: Mention that you would verify the status values and consider indexing the date and status columns for performance, showing awareness of real-world data and scalability.

1. Clarify requirements and assumptions

Confirm the meaning of 'completed', 'pending', and 'cancelled' statuses, and whether date is a DATE or TIMESTAMP. State any assumptions you make.

2. Choose conditional aggregation

Decide to use a single SELECT with GROUP BY date, using CASE expressions inside aggregate functions to compute each metric in one pass.

3. Write the SQL query

Construct the query: SELECT date, SUM(CASE WHEN status = 'completed' THEN amount ELSE 0 END) AS total_revenue, COUNT(CASE WHEN status = 'pending' THEN 1 END) AS pending_count, COUNT(CASE WHEN status = 'cancelled' THEN 1 END) AS cancelled_count FROM orders GROUP BY date;

4. Explain the query and performance

Walk through how the CASE statements work and why this is efficient (single table scan). Mention potential indexing on date and status.

5. Discuss edge cases and alternatives

Address handling of NULL amounts, unknown statuses, and alternative approaches like FILTER clause (if supported) or multiple subqueries, noting trade-offs.

Key Points to Mention

Conditional aggregation with CASE inside SUM/COUNT
Single table scan for efficiency
GROUP BY date to aggregate per day
Handling of NULLs in amount (e.g., COALESCE or ignore)
Indexing on date and status for performance
Portability across SQL dialects (e.g., FILTER clause in PostgreSQL)

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