Question 1

What are your salary expectations for this role?

Accepted Answer

**Situation**: I was negotiating with a FAANG-tier company after multiple rounds. **Task**: Communicate compensation expectations without anchoring low or pricing myself out. **Action**: I researched Levels.fyi, Blind, and Glassdoor for the role, level, and geo. I framed my response: "Based on market data for Staff/Principal Data Engineering in [location], total comp typically ranges $X–$Y....

Question 2

Where do you see yourself in your career five years from now?

Accepted Answer

**Situation**: In a Principal-level loop, the hiring manager asked about long-term trajectory. **Task**: Align my vision with the company's needs while showing ambition and impact focus. **Action**: I said: "In five years, I aim to be a Staff/Principal data engineer driving architectural decisions and scaling data platforms. I want to own data strategy for a significant domain—perhaps a business unit or company-wide—and be the go-to person for complex, high-stakes problems....

Question 3

Briefly introduce yourself and walk us through your journey as a Data Engineer so far.

Accepted Answer

**Situation**: I joined as a software engineer and saw data as a bottleneck—pipelines broke, nobody trusted the numbers. **Task**: Transition into data engineering and build reliable, scalable systems. **Action**: I moved from ETL dev to owning cloud data platforms—designed data lakes on AWS/GCP, optimized Spark jobs (reduced costs 40% via partition pruning and skew fixes), implemented Kafka/Flink streaming, and led migrations to Delta Lake....

Question 4

Can you explain the difference between OLTP and OLAP?

Accepted Answer

**OLTP**: Optimized for many small transactions (inserts, updates, deletes). Row-oriented, normalized, high concurrency. Examples: MySQL, PostgreSQL. **OLAP**: Optimized for complex analytical queries and aggregations on large datasets. Column-oriented, denormalized (star/snowflake). Examples: Snowflake, BigQuery, Redshift. **Why the split**: Different access patterns; mixing them degrades both. OLTP needs low latency and ACID; OLAP needs scan throughput....

Question 5

Explain the concept of ACID properties in the context of databases.

Accepted Answer

**ACID**: Atomicity (all or nothing), Consistency (valid state transitions), Isolation (concurrent transactions don't interfere), Durability (committed data persists). **Why it matters**: Without ACID, financial and operational data become inconsistent; retries and failures create duplicates or lost updates. **Scalability trade-off**: Strict isolation (e.g., Serializable) limits throughput; most OLTP systems use Read Committed or Repeatable Read....

Question 6

Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

Accepted Answer

**INNER JOIN**: Only rows with matches in both tables. **LEFT JOIN**: All from left; matches from right; NULLs where no match. **RIGHT JOIN**: All from right; matches from left. **FULL JOIN**: All from both; NULLs where no match. **Why it matters**: Join choice affects result cardinality and semantics. Wrong join = wrong numbers. **Scalability**: Hash joins are common; broadcast for small dimension. FULL OUTER can be expensive—large shuffle....

Question 7

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

Accepted Answer

**Approaches**: IS NULL / IS NOT NULL for filtering. **COALESCE(val1, val2, ...)**: First non-NULL value; useful for defaults. **NULLIF(val1, val2)**: Returns NULL if equal; e.g., NULLIF(divisor, 0) to avoid divide-by-zero. **Why it matters**: NULL propagates in expressions; aggregate functions ignore NULL (except COUNT(*)). JOIN on NULL yields no match (NULL ≠ NULL). **Scalability**: COALESCE in SELECT is cheap; in WHERE or JOIN it can prevent index use....

Question 8

What is a Common Table Expression (CTE), and when would you use it?

Accepted Answer

**CTE**: A named temporary result set in a WITH clause, referenced in the main query. **Use cases**: Readability—break complex queries into steps. Reusability—reference same CTE multiple times. Recursion—hierarchies (org chart, bills of materials). **Why it matters**: CTEs improve maintainability; deep subqueries are hard to debug. **Scalability**: In some engines (e.g., PostgreSQL), CTEs are optimization fences—materialized once. In others (Snowflake, BigQuery), they're inlined....

EPAM Data Engineer Interview Questions

+6 More Questions with Expert Answers

Other Companies

EPAM Data Engineer Interview Questions

+6 More Questions with Expert Answers

Other Companies