EPAM Data Engineer Interview Questions (2026)
Prepare for your technical rounds at EPAM with 38+ reported questions across SQL, Apache Spark, Python, and System Design. Compare typical candidate answers against FAANG 9/10 benchmarks, and test your own solutions with instant AI feedback.
Interview Topic Weightage at EPAM
Based on verified interview loopsUnlocked FAANG-Calibrated Answer Rubric (Top 3 Tested Questions)
- •Structured delivery following Problem Formulation -> Execution Plan -> Failure Recovery
- •Concrete parameters: partition keys, join strategies, memory limits, and I/O costs
- •Explicit trade-off evaluation (e.g. storage cost vs query latency, exactly-once overhead)
Complete EPAM Question Vault (35 Questions)
Showing 15 of 35Can you explain the difference between OLTP and OLAP?
OLTP (Online Transaction Processing) systems are optimized for many concurrent, small, atomic transactions (inserts, updates, deletes), ensuring data integrity and consistency. OLAP (Online Analytical…
Explain the concept of ACID properties in the context of databases.
ACID properties (Atomicity, Consistency, Isolation, Durability) are a set of principles that guarantee reliable transaction processing in databases, ensuring data integrity and reliability even amidst…
Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
INNER JOIN returns only rows with matches in both tables. LEFT JOIN returns all rows from the left table and matching rows from the right, with NULLs for non matches. RIGHT JOIN is symmetrical,…
How do you handle NULL values in SQL? Mention functions like COALESCE and NULLIF.
Handling NULL values in SQL is critical for data integrity and accurate query results, involving explicit filtering, substitution, and conditional assignment due to their unique behavior in…
What is a Common Table Expression (CTE), and when would you use it?
A Common Table Expression (CTE) is a named, temporary result set defined within a WITH clause, which can then be referenced multiple times within the main SQL query. It acts like a temporary view that…
What is the difference between a primary key and a unique key?
A primary key uniquely identifies each row in a table, disallows NULL values, and there can only be one per table. A unique key also enforces uniqueness across specified columns but allows a single…
What is the difference between WHERE and HAVING clauses in SQL?
The WHERE and HAVING clauses both filter data in SQL, but they operate at different stages of query execution and on different types of data. WHERE filters individual rows before any grouping or…
How do you handle conflicts within a team? Provide an example.
Situation: Two senior engineers disagreed on architecture: one advocated a single monolithic pipeline; the other wanted domain-split micro-pipelines. The debate stalled delivery and created tension. Task: Resolve the conflict with a data-driven decision that both could support. Action: I facilitated a structured session. I asked each to state their success criteria (maintainability, SLA, cost). I framed the choice as a trade-off: we ran benchmarks on a representative subset—monolith vs....
How do you handle data security and compliance in a cloud environment?
Security is layered: (1) Encryption: At rest (KMS-managed keys, SSE-S3, Azure Storage encryption) and in transit (TLS). Why: Compliance (GDPR, HIPAA) and breach mitigation. Trade-off: Key management adds latency and complexity; managed services reduce operational burden. (2) Access: Least-privilege IAM, role-based access, no long-lived keys in code. Use VPC/VNet for network isolation; private endpoints for data stores....
Why do you want to join EPAM?
I am particularly drawn to EPAM's global consulting model because it offers a unique blend of diverse technical challenges and accelerated professional growth that aligns perfectly with my career…
Describe a scenario where AWS Data Pipeline is preferred over Glue. Why?
Architectural logic: Data Pipeline = templated, EC2/EMR-managed; Glue = serverless Spark, Catalog, modern ETL. When Data Pipeline wins: (1) Cost—simple daily RDS→S3 copy can be cheaper than Glue DPUs; (2) Legacy—existing Pipeline definitions; (3) EC2/EMR coupling—Data Pipeline natively manages clusters for custom non-Spark code; (4) Simplicity—straight copy needs no Spark. Scalability: Data Pipeline scales via instance count; Glue via DPU....
Describe how you would use AWS Glue to schedule and manage Spark jobs.
Architectural logic: Glue Jobs = serverless Spark; Triggers = orchestration (schedule, event, on-demand). Flow: Define job (script, DPU, timeout) → add Trigger (cron, e.g., 0 6 * * ? *) → chain jobs if needed. Job bookmarks enable incremental processing. Scalability: DPU auto-scales; Triggers can fan-out. Cost: DPU-hours; over-provisioning wastes money....
Discuss the key differences between AWS Glue, Lambda, and Data Pipeline for orchestrating data workflows.
Architectural comparison: Glue = serverless Spark, Catalog, complex ETL. Lambda = event-driven, 15-min max, lightweight. Data Pipeline = templated, EC2/EMR-managed. When to use: Glue for data lake ETL; Lambda for triggers, small tasks; Data Pipeline for simple, cost-sensitive batch. Scalability: Glue via DPU; Lambda concurrency; Data Pipeline via instances. Cost: Lambda cheapest for small; Glue for Spark; Data Pipeline for simple batch....
Explain how AWS Glue interacts with on-premises SQL databases to extract data efficiently.
Architectural logic: Glue runs in AWS; connector/agent in on-prem network proxies connection. Efficient extraction: (1) Pushdown—filter at source. (2) Partition-based extract (date). (3) Incremental via bookmarks/watermark. (4) Parallel reads by splitting on numeric key. Why: Full scans over WAN are slow and expensive....
Explain when you would use Glue instead of Lambda for a data ingestion use case.
Architectural decision: Glue when—large data, Spark needed, >15 min, JDBC/Spark connectors. Lambda when—small payloads, event-driven, <15 min. Example: 100GB S3 transform → Glue. S3 put → validate → DynamoDB → Lambda....
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Frequently Asked Questions About EPAM Data Engineering Interviews
What topics are asked most frequently in EPAM data engineering interviews?▼
Technical interviews at EPAM heavily emphasize SQL and Cloud/Tools, accounting for the majority of technical questions in our repository of 38+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do EPAM interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at EPAM probe far beyond syntax. They expect candidates to discuss Spark DAG execution plans, shuffle partitions, join selection strategies (broadcast hash vs shuffle sort-merge), and handling un-even data distributions. In system design, emphasis is placed on fault tolerance, backfilling, and SLA preservation under high data volume.
What is the typical technical interview loop format for Data Engineers at EPAM?▼
The EPAM interview process typically consists of: 1) Initial Recruiter Screen (30 mins), 2) Technical Phone Screen focusing on live SQL and data manipulation (45-60 mins), followed by a 4-5 round Virtual Onsite covering SQL & Coding, Distributed Pipeline Architecture, Data Modeling, and a Behavioral/Bar-Raiser session.
How can I practice EPAM data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the EPAM library, submit your answer, and receive an instant FAANG-calibrated evaluation scored across accuracy, technical depth, communication, and completeness, complete with an interview-ready model answer rewrite.