Real, anonymous reports from people who interviewed for Data Analyst at American Express, newest first and distilled into what to expect across the loop.
My process dragged on for roughly 4–5 months, and it ended without a written offer even after I’d already been extended a verbal one. A recruiter reached out, and weeks later I went into a first-round conversation. The interviewer didn’t seem to know which role I’d applied for and used a different posting as the basis for the discussion. I corrected that, but the interview still went in the same direction. The questions felt vague, and when I asked for clarification, the answers were basically repeated instead of explained. I still got the impression they wanted to move forward.
My interview journey felt like a straightforward multi-step screening into technical and then people-focused evaluation. After the initial round, I went through a technical discussion where the focus landed on work experience and a mix of SQL, Python, and statistics. At times, puzzles showed up as well, so I didn’t get to rely only on data skills.
The interview process typically consists of multiple stages, starting with an online assessment followed by technical interviews and concluding with a behavioral round. Candidates reported timelines varying from a few days to several months, with some experiencing delays and inconsistent communication post-interview.
Technical interviews focus heavily on SQL, Python, and statistics, often involving live coding, problem-solving, and case studies that require candidates to demonstrate their practical application of these skills. Candidates should be prepared for questions on joins, window functions, and real-world data scenarios.
Behavioral interviews often utilize the STAR method to assess candidates' past experiences and how they handle teamwork, leadership, and problem-solving in collaborative settings. Candidates should be ready to discuss their projects and how they relate to the role's requirements.
Candidates noted varying levels of communication throughout the process, with some experiencing clear and supportive interactions while others faced abrupt or unclear feedback. It's important to seek clarity on timelines and expectations during the process.
The overall difficulty of the interviews is generally perceived as average to high, with candidates encouraged to prepare for both technical depth and conceptual understanding, particularly in machine learning and statistics. Familiarity with the role's specific tools and frameworks is crucial.
Some candidates experienced group assessments that tested teamwork and problem-solving under pressure, emphasizing the importance of collaboration skills alongside technical capabilities. This aspect may vary by interview format and should be considered in preparation.