- First round: Initial call with a recruiter who had come across my resume and thought I’d be a great fit for a new “hybrid” role combining data science and data engineering. - Second round: A technical interview focused on SQL and general programming. Since this was a new role, I went through a slightly different process than the typical data science path (for instance, there was no A/B testing component). - Third round: A conversation with the hiring manager covering motivation and behavioral questions. I also had the opportunity to ask detailed questions about the team and ongoing projects. - Fourth round (onsite): A mix of case studies, resume deep-dives, and behavioral interviews. - Final round: Meetings with executive leadership, where they dove deeper into specific areas based on feedback from the onsite interviews. The interview process was extremely positive. It was long (9 interviews in total), but each one had a clear purpose and was well-aligned with the role. The case studies were directly related to the projects I would be working on, and the interviewers had access to prior feedback, which helped avoid repetitive questions. The recruiters were also fantastic - very supportive, responsive, and provided helpful feedback after each round, including tips on what to focus on next.

Thorough multi-day interview process that asked different aspects of hard and soft skills. Overall a great interview experience compared to other big technology companies. Initial screen - given imperfect data around user, how to infer data gaps based on other signals. Main technical interview - SQL, experimentation, growth and product analytics metrics, chat with engineering partner and domain specific questions. Final C-level interview - prior product experiences, proudest achievements, how to communicate and drive product decisions.