I had mixed feelings after the interview process. It started off very smoothly; the initial email from the HR representative was professional, and the system for booking interview slots was straightforward and efficient.
The HR interview went well and lasted about an hour. It quickly became apparent, though, that the company operates with more of a startup mentality. This isn’t necessarily good or bad, but it’s worth noting for anyone expecting the level of structure found in larger organizations like Amazon or FAANG companies. The HR representative explained that the next interviews would be technical, involving both high-level and deep-dive discussions of expertise. However, I didn’t notice much distinction between those two in the next interviews.
The second interview was with team members and lasted around 1.5 hours. They had a prepared list of questions and went through them systematically. The questions covered a broad range of topics, including databases, Spark, data quality, data warehouses, data lakes, and basic machine learning. It went reasonably well, and at that point, it felt like there was a good match on both sides. The team members seemed enthusiastic and genuinely interested in their work, which left a positive impression. The following day, I received an email inviting me to the next interview with a manager.
After this, there was a week-long gap with no updates. It would have been helpful if the timeline had been communicated upfront. Eventually, I received an invitation to a two-hour (!) interview with a vague description, stating it would cover my background and experience. Surprisingly, about 70% of the interview focused on detailed Python-related questions, such as how Python is built and its modules — questions that seemed more appropriate for a software developer than someone applying for this role. Although the position was for a data engineering role, the manager mentioned they were looking for someone to optimize Python code.
Overall, it became apparent that the company was looking for one person to fill multiple roles rather than hiring specialists. The expectations for the role included:
- Data engineering expertise (e.g., databases, data warehouses, big data technologies like Spark, and pipelines).
- Cloud development skills (e.g., AWS).
- Advanced software development skills.
- ML/MLOps knowledge.
It seems clear that they’re hoping to find a single candidate with expertise across multiple domains, which is an ambitious goal. I’ll leave it to others to draw their own conclusions, but I’ll just say — good luck finding someone who meets all those requirements.
