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Bloomberg·Software Engineer·Technical Phone Screen·Senior

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Apr 2026

Summary

Bloomberg Data Engineer interview with a single SQL question that looked manageable until I remembered I couldn't just reach for BigQuery functions. The whole thing was a parsing exercise dressed up as a filtering and aggregation problem, and the cross-dialect comparison at the end was a nice twist I wasn't expecting.

Questions Asked (1)

Q1

You have a PostgreSQL table with a raw text column storing pipe-delimited key=value pairs. Write a single SQL query that parses the raw column into structured fields (user, country, timestamp), filters for US rows in August 2025, and returns the daily count of distinct users ordered by day. Also name the BigQuery functions you'd normally reach for and their PostgreSQL equivalents.

System DesignTechnical Trade-offsData Modeling
Author's notes

My brain went straight to BigQuery's REGEXP_EXTRACT and PARSE_TIMESTAMP before I even finished reading the question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the raw format and edge cases, then outline a step-by-step SQL strategy using string functions and aggregation. Write the query with CTEs for readability, and explicitly map BigQuery functions to PostgreSQL equivalents to show cross-platform fluency.

Pro tip: Mention that you'd validate the parsing logic on a small sample first and consider performance implications like indexing or materializing parsed columns for large-scale production use.

1. Clarify the raw format and requirements

Confirm the exact structure of the pipe-delimited key=value pairs, data types, and edge cases like missing keys or extra spaces. Restate the goal: parse user, country, timestamp; filter US and August 2025; return daily distinct user counts ordered by day.

2. Parse the raw column into structured fields

Use string functions to extract values for each key. In PostgreSQL, use split_part, substring with regular expressions, or a combination to reliably extract user, country, and timestamp.

3. Filter and aggregate

Apply WHERE conditions for country = 'US' and timestamp within August 2025. Then group by date (truncated to day) and count distinct users.

4. Order and format the output

Order the results by day ascending and ensure the date is properly formatted. Use a CTE to make the query readable and maintainable.

5. Map BigQuery functions to PostgreSQL equivalents

List common BigQuery functions like SPLIT, REGEXP_EXTRACT, PARSE_TIMESTAMP, and their PostgreSQL counterparts such as string_to_array, substring with regex, and to_timestamp.

Key Points to Mention

  • Use of split_part or regexp_match for parsing key=value pairs in PostgreSQL
  • Handling timestamp conversion and filtering for a specific month (August 2025)
  • Using COUNT(DISTINCT user) and DATE_TRUNC('day', timestamp) for daily distinct counts
  • BigQuery functions: SPLIT, REGEXP_EXTRACT, PARSE_TIMESTAMP; PostgreSQL equivalents: string_to_array, substring, to_timestamp
  • Performance considerations: indexing, materialized views, or pre-parsing for large datasets
  • Edge cases: missing keys, malformed rows, timezone handling for timestamps

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