Question 1

What is the difference between repartition and coalesce in Apache Spark?

Accepted Answer

**Repartition(n)**: Performs a full shuffle to redistribute data across exactly `n` partitions. Can increase or decrease partition count. Uses hash partitioning by default—all rows are exchanged across the network.

**Coalesce(n)**: Merges existing partitions into fewer partitions without a full shuffle. Only decreases partition count....

Question 2

Where do you see yourself in your career five years from now?

Accepted Answer

**Situation**: In a Principal-level loop, the hiring manager asked about long-term trajectory. **Task**: Align my vision with the company's needs while showing ambition and impact focus. **Action**: I said: "In five years, I aim to be a Staff/Principal data engineer driving architectural decisions and scaling data platforms. I want to own data strategy for a significant domain—perhaps a business unit or company-wide—and be the go-to person for complex, high-stakes problems....

Question 3

What are your professional development goals for the next five years?

Accepted Answer

Situation: 5-year development. Task: Concrete plan. Action: '(1) Distributed systems depth. (2) Leadership—initiatives, mentoring. (3) Open source, conferences. (4) Staff/Principal. (5) ML/data intersection.' Through projects, courses, stretch assignments....

Question 4

Why are you looking to switch jobs at this time?

Accepted Answer

Situation: Timing of switch. Task: Specific, positive. Action: Goals achieved; drawn to [company/role]; timing aligns with planning. Avoid: complaining, only compensation, vague. Connect to growth and fit....

Question 5

Why do you want to work at Puma, and how do you align with our company values?

Accepted Answer

Situation: Company + values. Task: Alignment. Action: Sports, innovation, global scale. Retail/e-commerce data. Values: performance, creativity, sustainability. Contribute to digital/physical data. Research; skills alignment....

Question 6

How does Z ORDERING enhance data retrieval performance?

Accepted Answer

**What**: Delta OPTIMIZE table ZORDER BY (cols)—physically clusters data. Space-filling curve (Z-order) for multi-column. Rows with similar values colocated.

**Why**: Query WHERE date='X' scans fewer files. Combine partition by date + Z-order by user_id. Trade-off: write cost; run OPTIMIZE periodically....

Question 7

What specifically attracts you to Puma as a company?

Accepted Answer

**Situation:** Built platforms for retail blending inventory, demand, customer analytics. **Task:** Unify data across 200+ stores and e-commerce for real-time stock. **Action:** CDC pipeline ERP+POS→lakehouse; demand forecasting; customer 360. **Result:** 25% fewer stockouts; MAPE 18%→12%. Puma's sports tech, omnichannel, and sustainability (supply chain carbon) present similar data challenges....

Question 8

How does Z ORDERING improve query performance in large datasets?

Accepted Answer

**Z-ordering** (Z-order curve): Colocates related data in same files. Multi-column filter (e.g., region + date) can skip more files vs. single-column partitioning. **Delta**: OPTIMIZE table ZORDER BY (col1, col2). **Why**: Improves file pruning; columnar still scans selected files. **Order**: High-filter columns first. **Combine**: Partition by date + Z-order by (region, user_id)....

Puma Data Engineer Interview Questions

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Puma Data Engineer Interview Questions

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

Other Companies