Question 1

Explain the differences between a Data Lake and a Data Warehouse.

Accepted Answer

**Data Lake**: Low-cost object storage (S3, ADLS) for raw, semi-structured, unstructured data. Schema-on-read; used for exploratory analytics, ML, archival. **Data Warehouse**: Structured, curated storage optimized for SQL; schema-on-write; used for BI and reporting. **Why both exist**: Lakes offer flexibility and cost at scale; warehouses offer query performance and concurrency....

Question 2

Explain your cloud-based data pipeline on AWS

Accepted Answer

Situation: Built data platform for analytics and ML. Architecture: S3 landing (API Gateway + Lambda, Kinesis Firehose) → Glue crawlers → Glue ETL (Parquet, curated) → Redshift/Athena. Streaming: Kinesis → Lambda/Kinesis Analytics → S3. Step Functions for multi-step. Medallion (raw/silver/gold). Result: Single lake; Glue Catalog; partition by date; encrypted; Terraform....

Question 3

Data Security in BFSI - encryption, IAM, auditing

Accepted Answer

**BFSI requirements**: Encryption at rest and in transit. IAM with MFA. Audit logging. Data masking for non-prod. Compliance: PCI-DSS, SOC2. Tokenization for PII. **Architecture**: Zero trust; classify data; least privilege....

Question 4

Data Storage and Retrieval Optimization techniques

Accepted Answer

**Techniques**: Partitioning (prune); columnar (Parquet, ORC); compression; indexing; caching. Query optimization (predicate pushdown, column pruning). Tier by access frequency. **Best practice**: Right format + partition + compression....

Question 5

Spark Coding: Using explode() Function to flatten nested arrays

Accepted Answer

**Why Explode:** Nested JSON (items array per order) → one row per item for joins/aggregation. Normalization for star schema.

**Functions:** explode()—one-to-many; null/empty arrays drop row. explode_outer()—keeps row, null for empty. posexplode()—adds position index. For multiple array cols: arrays_zip then explode.

**Scalability Risk:** Large arrays cause row explosion—1 row with 10K items → 10K rows. Can cause skew. Use explode then repartition or broadcast the small side for joins....

Question 6

Data Modeling and Airflow Scheduling - star schema, cron, backfill

Accepted Answer

**Architectural Logic**: Star schema + cron + backfill form a production batch pattern. **Star Schema**: Fact tables with conformed dimensions; optimizes BI query patterns. **Cron**: `schedule_interval='0 2 * * *'` (2 AM daily); align with source freshness. **Backfill**: `airflow dags backfill -s start_date -e end_date` or TriggerDagRun with execution_date. **Why catchup=False**: Prevents unintended historical runs; use explicit backfill DAG for controlled replays....

Question 7

Designing scalable data models - explain approach

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 8

Kafka Basics - architecture, topics, partitions, producers, consumers, Zookeeper

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in SQL is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Lumiq Data Engineer Interview Questions

+7 More Questions with Expert Answers

Other Companies

Lumiq Data Engineer Interview Questions

+7 More Questions with Expert Answers

Other Companies