Question 1

How would you collaborate with a product team to deliver a data feature?

Accepted Answer

Situation: Product needed experimentation dashboard; requirements unclear. Task: Partner to deliver iteratively. Action: I engaged in discovery, proposed 3 options with trade-offs, provided estimates. During build: weekly syncs, incremental delivery. I documented data model and SLAs....

Question 2

Tell me about a difficult challenge you faced in a data project and how you solved it

Accepted Answer

Situation: Migrating legacy Oracle/Java pipeline to Spark/S3 while maintaining zero downtime. Task: Validate and phase cutover. Action: Ran both systems in parallel; compared outputs. Incremental logic replication with unit tests. Built reconciliation framework. Phased cutover by dataset. Addressed schema drift and performance....

Question 3

Tell me about a time when a critical pipeline failed in production. What did you do?

Accepted Answer

Situation: Critical pipeline failed during revenue cycle. Task: Restore and improve. Action: Acknowledged in on-call channel; triaged (logs, UI, upstream). Root cause: upstream schema change. Implemented schema evolution; communicated every 30 mins. Backfill and validation. Post-mortem with action items. Implemented schema validation within a week....

Question 4

How do you decide what to automate or what to build from scratch?

Accepted Answer

**Framework**: (1) Frequency—recurring = automate; one-off = manual; (2) Complexity—simple/stable = automate; (3) ROI—effort vs time saved; (4) Existing tools—managed/OSS before custom; (5) Maintenance cost. Rule: automate after 3rd repetition. Build vs buy: buy when fits + support; build when differentiation....

Question 5

What makes you interested in working at Netflix?

Accepted Answer

**Situation:** I led data infrastructure for a streaming platform serving 50M+ users—personalization at scale, A/B test isolation, real-time analytics. **Task:** Architect pipelines powering recommendation models with sub-second SLAs. **Action:** Championed hybrid batch+stream architecture, schema evolution with backward compatibility, data quality gates. **Result:** 99.5% pipeline reliability; feature deployment cut from 2 weeks to 3 days....

Question 6

Write a SQL query to rank shows by daily viewership across different regions

Accepted Answer

SELECT show_id, region, date, viewership, RANK() OVER (PARTITION BY region, date ORDER BY viewership DESC) AS rank FROM viewership. Add show name via JOIN. **Why RANK**: Ties share rank (e.g., same viewership). DENSE_RANK for no gaps....

Question 7

How would you debug a slow-running PySpark job? What factors would you investigate?

Accepted Answer

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Debug slow PySpark job: (1) Spark UI—identify slow stages, skewed tasks (one task >> others), excessive shuffle. (2) Check data skew—use `df.groupBy('key').count()` to find hot keys. (3) Resource contention—executor memory, cores. (4) GC—high GC time in metrics. (5) Small files—many small reads....

Question 8

Write a transformation in PySpark to join and clean multiple raw input sources

Accepted Answer

**Why It Matters (Architectural Logic)**: Multi-source joins require consistent keys, null handling, and skew mitigation. Netflix-scale = partition by business dims, broadcast small tables.

Join and clean multiple sources with consistent keys and null handling:
```python
df1_clean = df1.dropDuplicates(["id"]).na.fill({"region": "Unknown"})
df2_clean = df2.filter(F.col("amount") > 0).dropDuplicates(["id"])
joined = df1_clean.join(df2_clean, "id", "left").drop(df2_clean.id)
final = joined.withCol...

Netflix Data Engineer Interview Questions

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Netflix Data Engineer Interview Questions

Reading isn't practice. Get AI feedback on your answers.

Other Companies