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Point72·Software Engineer·Online Assessment (OA)·Intermediate

Intermediate
Jun 2026

Summary

Point72 data engineer assessment, two independent coding tasks sent as an OA. One SQL, one PySpark. Nothing behavioral, just code.

Questions Asked (2)

Q1

Write a SQL query that, for each candidate, shows the top three distinct vote totals by state, including state names and counts formatted into place columns (1st, 2nd, 3rd). States tied at the same vote count should be grouped together and sorted alphabetically.

Data ModelingAlgorithms & Data Structures
Author's notes

This one took me longer than it should have.

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AI HintsAI Generated

Suggested Approach

Use a window function to rank distinct vote totals per candidate, then pivot the top three ranks into columns. Handle ties by grouping states with the same vote count and sorting them alphabetically within each rank.

Pro tip: Clarify whether 'top three distinct vote totals' means the three highest counts or the three most frequent counts; in interviews, stating your assumption shows attention to detail.

1. Clarify requirements

Confirm the definition of 'top three distinct vote totals' and how ties should be handled. Ensure you understand the expected output format.

2. Aggregate and rank

Group by candidate and state to get vote counts, then use DENSE_RANK() over candidate ordered by vote count descending to assign ranks to distinct totals.

3. Filter top three

Keep only rows where the dense rank is 1, 2, or 3. This ensures ties at the same vote count share the same rank.

4. Aggregate states per rank

For each candidate and rank, concatenate state names in alphabetical order into a single string (e.g., using STRING_AGG with ORDER BY).

5. Pivot to columns

Use conditional aggregation (CASE WHEN rank = 1 THEN states END) to pivot the ranks into separate columns for 1st, 2nd, and 3rd place.

Key Points to Mention

  • Use DENSE_RANK() to handle ties correctly, as it assigns the same rank to identical vote totals.
  • Group states with the same vote count per candidate before ranking to ensure distinct totals.
  • Sort state names alphabetically within each rank using ORDER BY in the aggregation function.
  • Pivot the ranked results into columns using conditional aggregation or a PIVOT clause.
  • Consider performance implications of window functions on large datasets and indexing strategies.
  • Test edge cases such as fewer than three distinct vote totals or multiple ties.

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

Q2

Implement several methods of a PySpark job class: initialize a Spark session, filter transactions to only valid ones based on account existence and balance rules, count distinct source accounts, and return the top 10 source accounts by transaction count as a dictionary.

Data ModelingSystem Design
Author's notes

The validation logic is three conditions joined together so it's mostly just joins and filters, nothing exotic.

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AI HintsAI Generated

Suggested Approach

Start by outlining the class structure and the purpose of each method, then implement them step by step, ensuring proper Spark session management and efficient DataFrame transformations. Focus on using PySpark's built-in functions for filtering, aggregation, and sorting to handle large datasets efficiently.

Pro tip: Use broadcast joins when filtering transactions against a smaller accounts dataset to avoid shuffles, and consider caching intermediate DataFrames if reused. Also, handle edge cases like null values and empty DataFrames gracefully.

1. Set up Spark session

Initialize a SparkSession with appropriate configurations (e.g., app name, master) and ensure it's accessible to other methods, possibly as a class attribute.

2. Filter valid transactions

Join transactions with accounts on account ID, then apply conditions: account exists and balance rules (e.g., balance >= transaction amount or balance > 0). Use inner join to keep only existing accounts.

3. Count distinct source accounts

After filtering, select the source account column and use distinct().count() to get the number of unique source accounts.

4. Compute top 10 source accounts

Group by source account, count transactions, order by count descending, limit to 10, and collect results as a list of rows.

5. Return as dictionary

Convert the collected rows into a dictionary mapping source account to transaction count, ensuring the format matches the requirement.

Key Points to Mention

  • Use of SparkSession.builder to create session and manage lifecycle
  • Efficient join strategies (e.g., broadcast join) for filtering
  • Handling of nulls and invalid data in balance rules
  • Use of groupBy, agg, orderBy, and limit for top-N computation
  • Conversion of DataFrame results to dictionary using collect and dict comprehension
  • Consideration of performance optimizations like caching and partitioning

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