Superhuman Analytics Engineer interview questions

The interview process consists of 7 (seven!) steps, including the TA 1:1 meeting, but not including ANY contact with the hiring manager - or none that was offered to me. Each stage is designed as a knock-off round as it evaluates just one competence. Candidates are eliminated if they are evaluated by the interviewer as "unsatisfactory" - the potential for bias here is enormous, as there is no structure, panel or holistic evaluation. The role is described in great detail to be an analytical engineering role, aspects of which I was eager to discuss about. However, the first two competence assessment stages were focused almost exclusively at insights delivery. During the second assessment, I had asked the interviewer if they could expand a little about "a week in their role", as I had only the job description to go buy thus far, not having met the hiring manager, and becoming somewhat confused about the role's function. They presented what was effectively a generalist BI role, and any question regarding analytical engineering topics (version control, pipeline testing etc) was answered "it is in our plans, but we are not there yet". I shared that I applied to the role reading that it is an analytical engineering role, and up to that point there were more hints that this is a BI role which is not what I expected or what I am interested in pursuing. The interviewer then shared that AE activities would make up about 80% of the time. Two days later I received a communication from the TA partner, sharing that despite positive assessment results, I would not be a good fit for the role because I am too interested in the analytical engineering part, which according to the hiring manager would be at most 50% of the role, the rest being comprised of supporting business stakeholders with analyses, insights and reporting. Sadly there seems to be a trend for companies to misappropriate the "analytical engineer" role to simply rename traditional BI responsibilities, adding none of the extended ones brought by the advent of the modern data stack. They still expect data professionals who apply to these engineering roles to effectively function as data generalists, and in this market many may swallow the pill - but many may do so also because in the beginning they were drawn by deceptive titles and job descriptions. It is quite sad to notice this trend on both sides of the ocean.