The process consists of 4 steps: TA call, meeting the hiring manager, presenting a case study + discussion, meeting a stakeholder. TA contacts were the most pleasant of the process, albeit there may be some communication delays. The hiring manager call consisted mostly of detailing prior experience and projects, with very little being shared in turn about their analytics team, structure, processes, needs, strategy, or about the position itself; all that was shared was painted in very broad lines and any specifics were avoided. The case study consisted of a "two-worded" style list of requirements and a CSV data dump with some data points being manually altered to muddy the data. The data is in the form of a highly aggregated report. Aside from the request to identify max 5 data quality issues and propose solutions to prevent them, the bulk of requirements were geared towards reporting and dashboarding. The case study's overall presentation and content did not fit in any way or form to the seniority desired for the role, nor was it designed to assess skills of an analytics engineer (AE), but rather those of a traditional junior-to-mid-level BI analyst who does not have any experience in working with the modern data stack. While presenting the case study, the hiring manager had a line of questioning hyper-focused on the data dump, analysis process and insights. When I pressed to underline if we're speaking about generalizations or the test itself, the answer was to dicuss the test. However, the feedback received after this step made generalizing statements about analytical processes and ethics, which were clearly not part of our discussion. An absolute shame that someone in middle management is unable to conduct professional interviews and assessments and underlines the dire need of INTERVIEW TRAINING across the entire Berlin tech-sphere. The company and/or hiring manager may have been confused over the candidate profile they need: traditional BI analyst vs analytical engineer, but all the while they "hijack" the role's name to attract a bigger pool of candidates only to sadly waste everyone's time in the process with virtually no gains. Data professionals having worked with modern data stacks and being interested in advancing their career on the analytical engineering path will not be a good fit for this position and likely for the team either.