← Back to Directory

HBO

Large Enterprises

HBO (Home Box Office) is an American premium television network and streaming service owned by Warner Bros. Discovery. It is renowned for producing acclaimed original programming such as Game of Thrones, The Sopranos, and Succession.

5 interview notes · updated Jul 2026

HBO·Software Engineer·Hiring Manager Screen

Jun 2026
Interviewed at HBO, got asked about Scrum experience. Pretty short and to the point, not much else to report.
  • What is your experience working with Scrum?

“Talked through sprint planning, retrospectives, the usual stuff.”

View Post

HBO·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed at HBO for what seemed like a data engineering or analytics role. One question about data collection and aggregation systems, nothing else to go on.
  • Walk me through your experience building or working with data collection and aggregation systems.

“Broad question and I kind of rambled.”

View Post

HBO·Data Scientist·Technical Phone Screen

May 2026
HBO Data Scientist technical screen, pretty deep Python OOP problem that felt more like a software engineering interview than anything data-related. One long multi-part question covering inheritance, properties, class methods, and testing patterns.
  • Given a Director class that inherits from Cast, implement an add_award method that's idempotent and avoids mutable default arguments, and expose awards only through a read-only, sorted property.
  • Add a static method to validate award name and year, and integrate it into the add_award logic.
  • Implement a classmethod constructor that builds a Director from a filmography dictionary, and explain how a subclass would handle additional fields.
  • Implement __eq__ and __hash__ so two Director instances with the same name are considered equal, and discuss the trade-offs of that design choice.
  • Write a short doctest-style example showing object creation, duplicate award handling, and the latest_award property.

“The idempotency part I got fine, just check before appending.”

View Post

HBO·Data Scientist·Technical Phone Screen

May 2026
HBO Data Scientist technical screen, all SQL, three fairly involved problems covering aggregations with window functions, experiment analysis, and a ranked filtering task. The schema was realistic enough that it felt like actual work rather than a toy exercise.
  • Given a multi-table schema with views, directors, and users, write SQL to compute unique viewers, total watch-hours, and median watch-time per viewer by director for August 2022. Multiple views by the same user for the same director count as one viewer, and the per-viewer median uses each user's summed seconds for that director.
  • For experiment HP123, compute 7-day post-exposure cancellation rates by variant. Only include users whose exposure happened before any cancellation, and count a cancellation as within-window if it falls strictly after exposure and within 7 days.
  • For August 2022, find the top 3 titles by unique viewers within each HP123 variant (A and B), but only among users who were exposed to the experiment before their first August view. Break ties by total watch-hours descending, then title alphabetically.

“The median part is what gets you.”

View Post

HBO·Data Scientist·Onsite - Cross-functional / Panel

May 2026
Interviewed for a Data Scientist role at HBO and the questions were heavier on strategy and org design than I expected. Less SQL, more 'explain a merger's second-order effects on your data team.' Felt like they were looking for someone who could operate at the intersection of analytics leadership and product thinking, not just someone who can run experiments.
  • Why do you want to work in streaming right now, and why this team specifically? How does your background connect to real user problems like discovery, personalization, or retention?
  • Compare Disney+ and Netflix across content strategy, pricing, bundling, international expansion, and data/ML maturity. What risks and opportunities does that competitive landscape create for HBO Max?
  • The Discovery and WarnerMedia merger created a combined org. What second-order effects would that have on the data team, things like metric standardization, experiment platform consolidation, taxonomy conflicts, and privacy compliance? Walk through a 90-day plan to harmonize metrics and experimentation guardrails.
  • Tell me about a time you had to deliver results while working across offices or with significant communication barriers. How did you keep everyone aligned and make sure people felt safe speaking up?
  • If a senior leader asks you to build a dashboard urgently, how do you figure out what decision it's actually supposed to inform, define what success looks like, and push back on scope without losing their trust?

“I had a decent answer prepped but I think I leaned too hard on 'I love content' vibes instead of tying it to actual data problems.”

View Post