Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Instacart data or analytics interview with a SQL-style question about ranking employees by office time, filtered to a specific job title. Short and focused, nothing too wild.

Questions Asked (1)

Given a dataset of employee office visit logs, find the top N employees by total time spent in the office, but only for a specific job title.

Algorithms & Data StructuresProduct Analytics & MetricsData Modeling

Author's notes

Pretty standard ranking query but the title filter is where people slip up if they apply it after the ranking instead of before.

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AI HintsAI Generated

Suggested Approach

Clarify the data schema and define 'time spent' as the sum of session durations per employee, then filter by job title and aggregate total time per employee. Use a min-heap of size N to efficiently find the top N employees without sorting the entire dataset.

Pro tip: Mention that you would handle edge cases like missing clock-out times or overlapping sessions, and discuss how to scale the solution for large datasets using distributed processing (e.g., MapReduce) or streaming if data is real-time.

1. Clarify requirements and data schema

Ask about the structure of visit logs (e.g., employee_id, job_title, clock_in, clock_out) and confirm that 'total time' means the sum of session durations. Also clarify if N is small and if the job title filter is exact or partial.

2. Compute session durations and filter by job title

For each log entry, calculate the duration (clock_out - clock_in) and filter out entries where job_title does not match the target. Handle invalid or missing timestamps appropriately.

3. Aggregate total time per employee

Group by employee_id and sum the durations to get total time spent in the office for each employee. Ensure that employees with multiple sessions are correctly combined.

4. Find top N employees efficiently

Use a min-heap of size N to track the top N employees by total time. Iterate through the aggregated results, pushing to the heap and popping the smallest when size exceeds N. This yields O(M log N) time, where M is the number of employees.

5. Return and validate results

Extract the top N employees from the heap, sort them in descending order of total time, and return. Discuss validation, such as checking for ties or ensuring N does not exceed the number of employees.

Key Points to Mention

Data cleaning: handling missing clock-out times, overlapping sessions, or timezone issues.
Efficient top-N selection using a min-heap (O(M log N)) versus full sort (O(M log M)).
Scalability: using MapReduce or Spark for large datasets, with map emitting (employee_id, duration) and reduce summing durations.
Product analytics context: defining 'time spent' as active time versus total logged time, and considering privacy or compliance.
Edge cases: employees with zero sessions, ties in total time, and N larger than the number of employees.
Time complexity and space complexity analysis of the chosen approach.

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