← Back to Directory

Newyorktimes

Large Enterprises

The New York Times is one of the most prominent and influential newspapers in the United States, known for its journalism, investigative reporting, and coverage of national and international news. It operates a major digital subscription business alongside its print publication and has expanded into products like games, cooking, and audio.

2 interview notes · updated Jul 2026

Newyorktimes·Data Analyst·Technical Phone Screen

May 2026
SQL-heavy technical screen for a Data Analyst role at the New York Times. Four questions, all around a single page_views table, ranging from basic aggregation to a window function problem that required careful tie-breaking logic. No behavioral stuff at all, which was a bit of a surprise.
  • Given a page_views table, compute the number of distinct content IDs viewed and the total view events for each device group (mobile vs desktop). Also explain whether those two numbers will typically be equal.
  • For each device type (phone, tab, desktop), find the top 3 hours of day by view volume. Break ties by preferring the earlier hour.
  • From the same table, return two numbers: the count of distinct non-null content IDs ever viewed, and the count of events where content_id is null.
  • For each agent and each calendar date, identify their first page view of that day using earliest timestamp and smallest view_id as a tiebreaker. Then compute the distribution of first-seen content types across all agent-days, including each type's share of total agent-days per date.

“Pretty straightforward GROUP BY with a CASE to bucket phone and tab together as mobile.”

View Post

Newyorktimes·Data Analyst·Technical Phone Screen

May 2026
SQL-heavy technical screen for a Data Analyst role at the New York Times, focused entirely on a page views fact table in BigQuery. Four queries back to back, each with its own wrinkle around NULLs, window functions, or aggregation grain. Felt more like a take-home that someone decided to do live.
  • Given a page_views table in BigQuery, write a query that returns mobile vs desktop unique contents viewed alongside total view events for each device group.
  • For each device group, find the top 3 hours of the day by view volume using window functions.
  • Write a single query that returns both the count of unique non-NULL content IDs and the count of view events where content_id is NULL.
  • For each agent and calendar day, identify the agent's first view event of the day (breaking ties by event_id), then compute the distribution of content types across all agent-days.

“The NULL handling is what trips people up here.”

View Post