Question 1

Conflict Resolution - how you handled project conflicts with teammates

Accepted Answer

Situation: A teammate insisted on full refresh for a large fact table; I advocated incremental loads for cost and speed. The disagreement escalated and risked project timelines. Task: Resolve the technical conflict without damaging the relationship. Action: I scheduled a 1:1, shared benchmarks (incremental 5x faster, ~80% cost savings, same correctness under our SCD2 contract). I proposed a pilot on one table and offered to own implementation and validation....

Question 2

Missed Timelines - instance of missing delivery timelines and how you communicated and delivered

Accepted Answer

STAR: SITUATION—Regulatory deadline for data pipeline; mid-sprint schema mismatch would delay delivery. TASK—Meet deadline while minimizing risk. ACTION—Escalated immediately to PM and compliance; proposed phased delivery—MVP (core fields) by deadline, full scope two weeks later; documented risks and got sign-off; delivered MVP on time; completed full scope in follow-up sprint. RESULT—Zero compliance breach; stakeholder trust maintained. COMMUNICATION: Transparent, early escalation....

Question 3

What would you do if a critical data pipeline failed during a holiday?

Accepted Answer

STAR: SITUATION—P1 on holiday. TASK—Restore. ACTION—Acknowledge; triage impact/root cause; mitigate (rollback/hotfix); communicate ETA; fix; retro after. Example: On-call; source API change; temp filter to unblock; full fix next day; added validation....

Question 4

Anagram Detection - find all anagrams from a given list of strings

Accepted Answer

**Logic:** Anagrams = same char counts. **Approach:** Group by sorted string or char-frequency tuple. **Python:** `groups = defaultdict(list); [groups[tuple(sorted(Counter(s).items()))].append(s) for s in words]`. O(n·k log k) for n words, k max length. **Alternative:** Sort each as key. **Why:** Deduplication, fuzzy matching....

Question 5

Find pairs with sum X from a list of numbers

Accepted Answer

**Hash set:** For each num, if (X-num) in seen, count pair. Add num to seen. O(n). **Duplicates:** Counter. **Why:** One pass. **Production:** Handle overflow; duplicates.

Question 6

Reverse a Linked List - implement solution for singly linked list

Accepted Answer

**Why In-Place Reversal:** O(n) time, O(1) space. Recursive version uses O(n) stack—fails for long lists (stack overflow).

**Algorithm:** Iterate, reverse next pointers: save next, point current to prev, advance prev and curr. Handles empty and single-node.

**Analogy in Data Eng:** Reverse order of events (LIFO processing), undo stacks....

Question 7

SQL Question - Cricket Scoreboard Table - aggregations, joins, grouping

Accepted Answer

Cricket scoreboard: tables for players, matches, innings, balls. Aggregations: runs per player (SUM), wickets (COUNT), strike rate (runs/balls*100). Joins: players to innings to balls. Group by match, player. Example: SELECT player_id, SUM(runs), COUNT(CASE WHEN wicket THEN 1 END) FROM balls GROUP BY player_id, match_id. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....

Question 8

SQL Question - Inner Join with Null Handling, compute total row count

Accepted Answer

INNER JOIN with NULL: NULL keys don't match (NULL = NULL is false), so they're excluded. Count: SELECT COUNT(*) FROM a INNER JOIN b ON a.key = b.key. If key has nulls, inner join count is less than min(|a|,|b|). To include null matches: ON (a.key = b.key OR (a.key IS NULL AND b.key IS NULL)). Count depends on null handling. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....

Question 9

Apache Spark Fundamentals - failures, job optimization, resource utilization

Accepted Answer

**Why this matters at scale**: Failures and misconfig cost time and money; optimization compounds across thousands of jobs. **Failures & resolutions**: Executor OOM—increase memory, reduce partition size, or optimize (avoid broad joins). Driver OOM—avoid collect(); use checkpointing for long lineage. Stragglers—speculative execution, salting for skew. **Optimization**: Partition pruning, predicate pushdown, broadcast joins, AQE....

Question 10

PySpark Coding Challenge - dataset with 4-5 columns, solve data processing problem on paper

Accepted Answer

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

PySpark on-paper challenge: Read data, identify columns, state transformations (filter, join, agg), write pseudocode or actual PySpark. Example: Given id, amount, date—compute daily sum per id. `df.groupBy('id','date').sum('amount')`....

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