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Meta·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Meta data engineer technical screen with two problems back to back, one Python and one SQL. Nothing too wild but the Python one had a wrinkle I didn't fully anticipate going in.

Questions Asked (2)

Q1

Implement a function that takes an arbitrarily nested list of integers and returns a flat list in left-to-right order. Recursion is off the table if nesting can be very deep. Include complexity analysis and tests for edge cases like empty input, deep nesting, and bad element types.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The no-recursion constraint is what tripped me up at first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose an iterative solution using an explicit stack to avoid recursion limits. Walk through the algorithm, analyze time and space complexity, and discuss test cases including empty input, deep nesting, and invalid types.

Pro tip: Mention that Python's default recursion limit (~1000) makes recursion risky for deep nesting, and that an explicit stack is safer. Also, discuss how to handle non-integer elements gracefully, such as raising a TypeError with a clear message.

1. Clarify requirements and edge cases

Ask about input constraints (e.g., maximum depth, element types) and expected behavior for invalid inputs. Confirm that the output should be a flat list of integers in left-to-right order.

2. Design iterative approach

Propose using an explicit stack to simulate recursion, pushing elements in reverse order to maintain left-to-right traversal. Alternatively, use a queue for BFS if order doesn't matter, but specify that order matters here.

3. Implement and handle edge cases

Write code that checks if an element is a list; if so, push its elements onto the stack in reverse. If it's an integer, append to result. For other types, raise a TypeError with a descriptive message.

4. Analyze complexity

State that time complexity is O(N) where N is total number of elements (including nested lists), and space complexity is O(D) for the stack, where D is the maximum depth, plus O(N) for the output list.

5. Discuss tests and trade-offs

Outline test cases: empty list, single element, deep nesting (e.g., 10,000 levels), mixed types, and large flat list. Compare iterative vs recursive approaches, noting recursion risks stack overflow.

Key Points to Mention

  • Use an explicit stack to avoid recursion depth limits.
  • Maintain left-to-right order by pushing nested elements in reverse.
  • Time complexity O(N) and space complexity O(D) for stack, where D is max depth.
  • Handle invalid element types by raising TypeError with clear message.
  • Test edge cases: empty input, deep nesting, mixed types, and large inputs.
  • Discuss trade-offs: iterative is safer for deep nesting but slightly more complex than recursion.

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

Q2

Write a SQL query against an events table with user_id, event_time, and event_type columns. Return the count of distinct active users per UTC calendar date. Output should have columns event_date and dau.

Product Analytics & MetricsData Modeling
Author's notes

Pretty standard DAU query.

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AI HintsAI Generated

Suggested Approach

Write a single SELECT that truncates event_time to the UTC calendar date and counts distinct user_id values, grouping by that date. Use DATE(event_time) or CAST(event_time AS DATE) for the truncation, and COUNT(DISTINCT user_id) for the daily active user metric.

Pro tip: Clarify that 'active' means any event, and mention that if event_time is stored with a timezone, you must convert to UTC first (e.g., AT TIME ZONE 'UTC') to avoid off-by-one date errors.

1. Clarify requirements and edge cases

Confirm that 'active' means any event, that the date is based on UTC, and that the output should be one row per calendar date with no gaps.

2. Extract the UTC date

Use DATE(event_time) or CAST(event_time AS DATE) to truncate the timestamp to a date. If the column is timezone-aware, convert to UTC first.

3. Count distinct users per date

Apply COUNT(DISTINCT user_id) to count unique active users for each date, ensuring duplicates from multiple events are collapsed.

4. Group and order the results

Group by the extracted date and order by event_date ascending for a clean, chronological output.

5. Write and validate the final query

Assemble the query, alias columns as event_date and dau, and mentally test with sample data to confirm correctness.

Key Points to Mention

  • COUNT(DISTINCT user_id) to avoid double-counting users with multiple events per day
  • Date truncation using DATE() or CAST to a date type, ensuring UTC alignment
  • Handling timezone conversion if event_time is not already in UTC
  • Grouping by the derived date column and ordering chronologically
  • Performance considerations: indexing on event_time and user_id for large datasets
  • Edge cases: null user_ids, events outside the expected range, and dates with zero activity

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