🔶1. The bait-and-switch JD. I applied for a Senior Data Analytics Engineer role. The job description listed dashboards, descriptive and inferential statistics, stakeholder communication, FastAPI, Streamlit, and Jupyter as key responsibilities and must-haves. The mandatory pre-screening 50 min test was roughly half Data Science and LLMs. The interviewer confirmed he wrote both the JD and the test himself. Then during the interview he immediately contradicted everything, calling this "purely an engineering role" and questioning why I mentioned analytics. 🔶2. Aggression, not evaluation. No matter how much disagreement there may be during a technical discussion, raising your voice and delivering personal reprimands is not professional conduct. The interviewer's tone was aggressive throughout, escalating during any disagreement. He read requirements from a printed page for ten minutes - including soft skills like "be proactive." He extended the meeting ten minutes past schedule without asking, leaving zero time for candidate questions. 🔶 3. Lectures masking shallow knowledge. He spent another ten minutes explaining why Glue + Athena is the best architecture in existence, dismissing Snowflake, Databricks, and Redshift as "too expensive" or "inefficient" - apparently unaware that Redshift Serverless or ClickHouse are actual AdTech standards. He presented 1TB of data as an unprecedented challenge to a candidate with documented 30M+ daily event processing experience. He confused database terminology during follow-ups, correcting me where his own understanding was visibly limited. 🔶 4. No orchestrator, no DWH, DuckDB in production. He confirmed the team has no Airflow, Dagster, or Prefect. Pipelines run on AWS StepFunctions - a microservice state machine with no backfill, no cross-pipeline dependencies, no DAG monitoring. Athena is a query engine, not a warehouse. DuckDB - an embedded analytics database designed for local experimentation - is part of their production stack. The JD lists Redshift, Snowflake, BigQuery, and Databricks as requirements, but they use none of them. When I pointed out the JD listed ChatGPT, Claude and Ollama as required tools, the interviewer visibly froze, then admitted only programmers use Amazon Q - the free AWS tool. Penny wise, pound foolish: saving on AWS bills while wasting engineering hours on manual orchestration, retries, backfills, and monitoring that any standard tool handles out of the box. 🔶 5. The JD was rewritten after my process. Dashboards moved from key responsibility to nice-to-have marked "not the core function." Statistics removed entirely. Airflow moved from core stack to nice-to-have. A new sentence added in bold: "This is not a Data Analyst role." They also posted a Staff DAE requiring 7+ years, TDD mastery, functional programming, polyglot skills in Scala, Go, C++, and Java - for a team of five running StepFunctions and DuckDB with no orchestrator and no warehouse. 🔶 6. The process demands everything, offers nothing. Beyond the pre-screening test and the manager interview, the remaining stages include a home assignment with a strict two-day deadline, a one-hour live coding session, and a team interview with additional technical questions. At no point during the process did the company provide any clarity on salary range or budget - expecting candidates to invest weeks without knowing if compensation is even on the table. 🔶 7. GDPR violations confirmed. I asked about the June 2025 Netzpolitik.org report confirming the Berlin Data Protection Authority found significant GDPR violations at Adsquare after an on-site audit. The interviewer deflected without substance - concerning for a company whose entire product is built on personal location data. 🔶 8. The interview revealed that the technical evaluation was conducted by someone with a BI and analytics management background, not software engineering. This explains the lectures on recently learned topics, the terminology confusion, and the hostility toward any independent technical perspective. This is not a role for a senior peer.