Question 1

Discuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.

Accepted Answer

**ROW_NUMBER()**: Unique sequential numbers (1, 2, 3...); no ties—deterministic only with ORDER BY uniqueness. **RANK()**: Same rank for ties; skips (1, 2, 2, 4). **DENSE_RANK()**: Same rank for ties; no gaps (1, 2, 2, 3). **Project examples**: ROW_NUMBER() to deduplicate events by (user_id, event_time) keeping first—critical when upstream sends duplicates. DENSE_RANK() for 'top 10 products per category' reports—avoids gaps when filtering....

Question 2

Describe a time when you had to optimize a slow SQL query. What steps did you take?

Accepted Answer

**Situation**: A critical exec report was timing out at 30+ minutes; SLA was 5 minutes. **Task**: Diagnose and fix without changing business logic. **Action**: I ran EXPLAIN (ANALYZE) and found: (1) missing index on join key causing full table scan, (2) cross join that could be inner join with a filter, (3) filter in HAVING that could move to WHERE to reduce rows early, (4) unnecessary ORDER BY on a subquery....

Question 3

Why are you leaving your current company?

Accepted Answer

I've grown significantly at [Current Company]—I led [concrete achievement, e.g., migration of X TB, reduction of pipeline cost by Y%]. I'm proud of what we built. However, I'm at a point where I want to deepen my impact: I'm looking for a role where I can own architecture for systems at a larger scale, work with [specific tech: e.g., real-time streaming at petabyte scale], and mentor other engineers. [Target Company]'s work in [specific area—cite a blog, product, or news] aligns with that....

Question 4

Have you worked on Data Warehousing projects?

Accepted Answer

**Architectural context**: A data warehouse is the semantic layer between raw data and business decisions. Design choices—star vs snowflake, SCD strategy, partitioning—directly impact query latency, storage cost, and maintenance burden. **Key responsibilities**: (1) **Schema design**: Star for BI simplicity, snowflake for normalized flexibility. SCD Type 2 for slowly changing dimensions (audit trail, point-in-time correctness)....

Question 5

What is the difference between OLTP and OLAP?

Accepted Answer

**Why the distinction exists**: They serve different access patterns. OLTP = many small, random writes and point reads. OLAP = few, large sequential scans and aggregations. Optimizing for one degrades the other. **OLTP**: Row-oriented storage (fast single-row access). Normalized schema (3NF) to avoid update anomalies. Indexes for lookup (B-tree). ACID for consistency. High concurrency via locking/mvcc. Examples: PostgreSQL, MySQL, Oracle....

Question 6

What is the difference between SQL and NoSQL databases?

Accepted Answer

**Why both exist**: SQL excels at structured data, complex joins, and strong consistency. NoSQL excels at unstructured/semi-structured data, horizontal scale, and flexible schema. **SQL (relational)**: Fixed schema, ACID, vertical scale (or managed horizontal via Citus, etc.). Optimized for joins and aggregations. Best for: transactional systems, reporting, anything requiring referential integrity. **NoSQL**: Schema-flexible, BASE (eventual consistency), horizontal scale....

Question 7

Explain Common Table Expressions (CTEs) and their benefits.

Accepted Answer

**Architectural Logic**: CTEs are named subqueries in a WITH clause, evaluated as defined (or materialized, depending on engine). They provide logical decomposition without forcing physical materialization. **Why**: Readability and reuse—complex pipelines split into stages (raw → cleansed → aggregated). Recursion for hierarchies (org charts, bill-of-materials). Some engines inline CTEs; others (e.g., BigQuery) can materialize for reuse....

Question 8

Explain SQL Window Functions with examples.

Accepted Answer

**Architectural Logic**: Window functions compute over a "frame" of rows related to the current row without collapsing rows. Syntax: func() OVER (PARTITION BY ... ORDER BY ... [frame]). Categories: Ranking (ROW_NUMBER, RANK, DENSE_RANK), Aggregate (SUM, AVG over partitions), Value (LAG, LEAD, FIRST_VALUE). **Why**: Enable row-level analytics (running totals, moving averages, prior/next comparisons) without self-joins. Self-joins duplicate data and are slower....

Question 9

Explain the use of the MERGE statement in SQL.

Accepted Answer

**Architectural Logic**: MERGE (upsert) performs INSERT, UPDATE, DELETE in one atomic statement based on a join condition. Syntax: MERGE INTO target USING source ON (key) WHEN MATCHED THEN UPDATE ... WHEN NOT MATCHED THEN INSERT ... [WHEN NOT MATCHED BY SOURCE THEN DELETE]. **Why**: Single pass over target and source; avoids read-modify-write race conditions; efficient for SCD Type 1/2, incremental loads, CDC sync. **Scalability**: Join key should be indexed; large source scans can lock target....

Question 10

How do you handle NULL values in SQL? Mention functions like COALESCE and ISNULL.

Accepted Answer

**Architectural Logic**: NULL represents unknown/missing; it propagates through expressions (NULL + 1 = NULL). Handling: IS NULL / IS NOT NULL for predicates; COALESCE(val1, val2, ...) for first non-NULL (portable); ISNULL/IFNULL for dialect-specific default; NULLIF(val1, val2) to normalize to NULL. **Why**: JOIN on NULL yields no match (NULL != NULL). Aggregates ignore NULL except COUNT(*). Explicit handling prevents silent exclusions....

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