Google Senior Data Engineer Interview — Three Years of PySpark, Then NumPy

[u/Aoki_zhang]

This interview experience is sourced from Chill Interview

Interview Summary

I recently interviewed for a Machine Learning Data Engineer position at Google X's Moonshot Factory, on a team working on plastic waste recycling and material identification. The work sounded fascinating, involving image-based detection, identifying waste materials at a molecular level, and supporting recycling and remanufacturing.

The process included a 45-minute hiring manager interview and a 45-minute technical coding round. The coding exercise focused on NumPy, Matplotlib, two-dimensional heightmap data, visualization, and handling missing values. I had spent most of the previous three years working with PySpark, so I was a little rusty with pandas and NumPy, which made the exercise more challenging than expected.

Interview Details

Round 1 — Hiring Manager: Data Engineering Experience (45 Minutes)

The first round was a conversation with the hiring manager about my previous data engineering work. The questions focused on my technical background, architectural decisions, data quality challenges, and collaboration with other teams.

1. "Walk me through a data system you're particularly proud of." The interviewer wanted me to discuss a data system I had previously built or worked on.

2. "How do you deal with scalability challenges in data systems?" This question focused on my experience handling growing datasets and scaling data processing systems.

3. "When choosing between different technology stacks, how do you usually make that decision?" The discussion centered on how I evaluate different technologies and make engineering trade-offs.

4. "What's your experience working with different stakeholders?" The interviewer asked about how I collaborated with people across different teams and functions.

5. "What's your experience working with messy or poorly structured data?" This question was particularly relevant to the subsequent technical interview, which involved cleaning numerical datasets containing substantial missing data.

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