Question 1

Can you share an experience where you resolved a conflict within your team?

Accepted Answer

**Situation:** Two senior engineers disagreed on Airflow vs Prefect for a new project; 1-week delay.

**Task:** As lead, I needed to resolve and align the team.

**Action:** I set up a bake-off. Each presented a PoC for our core use case. We defined criteria: community support, testing ease, CI/CD integration. I moderated, focusing on evidence over opinion. We scored each tool.

**Result:** We chose Airflow for better fit. Team aligned, project delivered on time....

Question 2

Create a SQL query to identify customers with purchases above a dynamic threshold.

Accepted Answer

**Architectural Logic**: Dynamic thresholds require subquery/CTE; efficiency depends on threshold computation. **Approach**: `WITH agg AS (SELECT customer_id, SUM(amount) AS total FROM purchases GROUP BY customer_id), threshold AS (SELECT percentile_cont(0.9) WITHIN GROUP (ORDER BY total) AS p90 FROM agg) SELECT a.* FROM agg a, threshold t WHERE a.total > t.p90`. **Why CTE**: Reusable threshold; single scan for percentile....

Question 3

How do you monitor consumer lag in Kafka, and how can you reduce it?

Accepted Answer

**Monitor**: Lag = log end offset - consumer offset. Tools: Kafka Consumer API, JMX, Burrow, Grafana. **Reduce lag**: (1) Scale consumers (same group)—more partitions. (2) Increase partitions (requires rebalance). (3) Optimize processing—async I/O, batch. (4) Scale downstream—don't block on slow DB. (5) Temporarily add consumer instances. **Alert**: Lag > 10K or threshold. **Root cause**: Slow processing vs....

Question 4

How do you optimize partitioning when dealing with large datasets?

Accepted Answer

**Principles**: (1) Partition by high-cardinality filter columns (date, region). (2) Target 128–200MB per partition. (3) Avoid over-partitioning—small-file problem. (4) Coalesce before write. (5) Z-ordering (Delta) for multi-column filters. (6) Dynamic partition pruning. **Align with queries**: Analyze query filters. **Repartition**: By join key before shuffle....

Question 5

How would you deal with a situation where you had to work with a difficult team member?

Accepted Answer

**Situation**: Teammate missed deadlines; resisted code review. **Task**: Deliver project; improve collaboration. **Action**: (1) 1:1 to understand perspective—overload? unclear expectations? (2) Aligned on goals: project success. (3) Set clear expectations, deadlines. (4) Data-backed feedback: "Last 3 PRs took 5 days to merge; can we target 2?" (5) Escalated to manager after repeated attempts. **Result**: Communication improved; deadlines met; member engaged with feedback....

Question 6

Optimize a query fetching customer data with a rolling 6-month sales sum.

Accepted Answer

Rolling 6-month sum: use window functions. SELECT customer_id, SUM(amount) OVER (PARTITION BY customer_id ORDER BY sale_date ROWS BETWEEN 5 PRECEDING AND CURRENT ROW) AS rolling_6m FROM sales. Or RANGE for actual 6 months. Optimize: partition table by customer_id or date; index on (customer_id, sale_date). Pre-aggregate in summary table if queried often. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....

Question 7

What is your notice period, and are you interviewing elsewhere?

Accepted Answer

**Situation**: Context of the challenge. **Task**: Your responsibility. **Action**: Specific steps, tools, collaboration. **Result**: Quantified outcome. Answer authentically and professionally. Example: "My notice period is X weeks. I'm exploring opportunities that align with my career goals—[this role/company] is among my top choices because of [specific reason]....

Question 8

What optimizations would you apply for partitioning strategies?

Accepted Answer

Partition optimization is a critical lever for cost and performance at scale. **Why it matters**: Misaligned partitions cause full-table scans; over-partitioning creates small-file hell (each file = metadata overhead + slower I/O). **Strategies**: (1) Align partition key with query filters—date, region—so the engine prunes at scan time. (2) Target 100MB–1GB per partition; under that, compaction merges small files....

Question 9

What technologies are you most comfortable with?

Accepted Answer

**Situation**: As a senior data engineer, I've worked across SQL, Spark, orchestration, and cloud. **Task**: Deliver reliable pipelines that scale. **Action**: Core stack: SQL (Snowflake, BigQuery, Spark SQL), Python, Spark/PySpark, dbt, Airflow/Prefect, cloud (AWS/GCP). Production experience: streaming (Kafka), lakehouse (Delta), ML pipelines. I focus on fundamentals—modeling, testing, observability—that transfer across tools....

Question 10

Write a SQL query to find employees earning the second-highest salary.

Accepted Answer

SELECT salary FROM employees ORDER BY salary DESC LIMIT 1 OFFSET 1. Or: SELECT MAX(salary) FROM employees WHERE salary < (SELECT MAX(salary) FROM employees). Or DENSE_RANK: SELECT salary FROM (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) r FROM employees) WHERE r = 2. **Edge case**: Fewer than two employees—returns empty....

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