Question 1

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 2

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 3

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 4

Explain your projects on which you worked till now and what was your role?

Accepted Answer

**Projects**: (1) Customer 360—unify CRM, transactions, behavior for Dunnhumby-style analytics; (2) Recommendation pipeline—feature computation, model serving; (3) Platform migration—Hadoop to cloud.

**Role**: Designed models; implemented ETL (Spark, SQL); data quality; supported analysts. Led schema design, performance tuning, documentation....

Question 5

Name the tools and technologies you have worked with to date.

Accepted Answer

Structure by category with depth. Processing: Python, PySpark, Scala; SQL (BigQuery, Snowflake, PostgreSQL). Orchestration: Airflow, Prefect, Dagster. Cloud: AWS (S3, Glue, Lambda, EMR), GCP (BigQuery, Dataflow). Storage: Parquet, Delta Lake, Iceberg. Streaming: Kafka, Kinesis. MLOps: MLflow, Feature Store. WHY: Demonstrate breadth and alignment with role. Differentiate: 'I've led migrations to dbt' vs....

Question 6

What steps do you take to troubleshoot a slow-running Spark job?

Accepted Answer

(1) Spark UI—slow stages/tasks. (2) Skew—one task long; salting. (3) Shuffle—volume; broadcast joins. (4) Spill—memory; increase or partitions. (5) GC—pause; tune fractions. (6) Code—UDFs, cartesian. (7) Plan—EXPLAIN, predicate pushdown. Order: Profile with representative data; biggest bottleneck first....

Question 7

What would you do if you were assigned a task with a technology you've never used before?

Accepted Answer

STAR: SITUATION—Task with [Tool]. TASK—Deliver. ACTION—(1) Research—docs, tutorials. (2) PoC—small experiment. (3) Mentor—ask experts. (4) Implement—incremental. (5) Document—share. Example: dbt first time—3 days Fundamentals, small model, review, full migration, 'dbt at [Company]' guide....

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