EY Data Engineer Interview Questions (2026)
Prepare for your technical rounds at EY with 24+ 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 EY
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 EY Question Vault (21 Questions)
Showing 15 of 21Explain the difference between Azure Data Factory (ADF) and Databricks.
ADF is an orchestration and data-movement service; Databricks is a compute platform for analytics and ML. Why it matters: ADF excels at scheduling, branching, retries, and connectors—it's the 'conductor.' Databricks excels at heavy transforms (Spark), Delta Lake, and ML—it's the 'orchestra.' Scalability: ADF scales by parallelism (activities, self-hosted IR nodes); Databricks scales via cluster sizing and auto-scaling....
What are the key components of AWS Glue, and how do they work together?
Glue Catalog: Central metadata store (Hive-compatible)—enables querying S3 data via Athena/Redshift Spectrum without moving it. Glue Crawlers: Schema discovery and Catalog population—useful for ad-hoc sources; at scale, prefer schema-as-code to avoid crawler cost and drift. Glue ETL Jobs: Serverless Spark for transforms; auto-scaling, pay-per-DPU. Glue DataBrew: Visual prep for non-engineers. Glue Schema Registry: Schema evolution for streaming (Kafka, Kinesis)....
What is Azure Data Factory (ADF), and what are its main components?
ADF is a cloud-native data integration service for orchestration and movement. Components: Pipelines (logical groups of activities), Activities (Copy, Lookup, Databricks, Data Flow), Datasets (structure definitions), Linked Services (connection configs), Triggers (schedule or event-based), Integration Runtime (IR—compute for execution). Flow: Linked Service -> Dataset -> Activity -> Pipeline -> Trigger....
What is the difference between S3 and HDFS?
S3: Object storage—durable, highly available, decoupled from compute. Pay-per-use, virtually unlimited scale. Eventual consistency (now strong for new overwrites). No data locality. HDFS: Distributed file system—block-based, colocated with compute nodes. Data locality reduces network I/O; strong consistency. Requires cluster management. Why it matters: S3 enables cloud-native, serverless patterns (Lambda, Glue, Athena); HDFS optimizes for batch processing where locality matters....
What is the role of AWS Lambda in a data engineering pipeline?
Lambda is serverless compute for event-driven, short-lived tasks. Roles: (1) Orchestration: Trigger Glue, EMR, or Step Functions when files land (S3), events arrive (Kinesis), or schedules fire. (2) Light transforms: JSON→Parquet, small aggregations, API calls. (3) Alerts: Validate data, send Slack/PagerDuty on anomalies. Why: No server management; scale to thousands of concurrent invocations; pay per request and duration. Limitations: 15-min timeout, 10GB memory, 6MB payload (sync)....
What is the role of the Integration Runtime (IR) in ADF?
IR is the compute layer that executes ADF activities—data movement, transformations, and external calls. Types: (1) Azure IR: Managed, cloud-native—for cloud-to-cloud copies, Data Flows, Databricks calls. No maintenance. (2) Self-hosted IR: Install on your VM or container—for on-prem, VNet, or private data sources. You manage scaling and uptime. (3) Azure-SSIS IR: For running SSIS packages in Azure. Why it matters: Data never flows through ADF; it flows through IR. Network topology (cloud vs....
Difference Between Internal and External Tables in BigQuery
**Architectural Logic**: Internal (native) tables: BigQuery owns metadata and storage (Colossus). Columnar layout, partitioning, clustering, automatic optimization. Dropping = data deleted. External tables: BigQuery owns metadata only; data lives in GCS, Drive, Bigtable. Dropping = metadata deleted, data persists. **Why**: Internal enables DML, streaming inserts, partitioning strategies, cost-effective storage classes....
What is the difference between OLTP and OLAP?
OLTP (Online Transaction Processing) systems are optimized for high frequency, concurrent, small transactional operations (reads, writes, updates, deletes), ensuring data integrity. OLAP (Online…
Describe AWS Glue components and their functions.
Why Glue: Serverless ETL on AWS—no cluster management; pay per DPU. Architectural logic: (1) Data Catalog—metadata; table definitions. (2) Crawlers—discover schema; populate catalog. (3) ETL Jobs—Spark or Python; transform. (4) Workflows—orchestrate jobs and crawlers. (5) DataBrew—visual prep. (6) Schema Registry—streaming schema evolution. (7) Interactive Sessions—notebook dev. Scalability: Serverless; auto-scales. Cost: DPU hours; crawlers and jobs separate....
Reverse a string with special characters preserved.
**Why Preserve Special Chars:** Log parsing (reverse for suffix search), obfuscation (mask emails but keep structure), and data cleaning where delimiters must stay. **Two-Pointer:** Swap only when both i and j point to letters. Advance when hitting non-letter. O(n), single pass....
Write Python code to remove duplicates from a string.
The most Pythonic and efficient way to remove duplicate characters from a string while preserving their original order is by leveraging dict.fromkeys() or an explicit ordered set like approach. Both…
Explain BigQuery Architecture.
**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....
Explain Data Modeling SCD Types (Type 1, 2, 3).
**Architectural Logic**: SCD types trade history depth vs storage and complexity. **Type 1**: Overwrite; no history. Use when history irrelevant (typo correction). **Type 2**: Full history; new row on change; effective_from, effective_to, current_flag. Use when history needed (customer address, department). **Type 3**: Limited history; previous value in column. Use when only last change matters. **Implementation**: Type 1 = UPDATE; Type 2 = INSERT new + UPDATE current; Type 3 = UPDATE shift....
How many records result from Inner Join, Left Join, Right Join given Table A and Table B?
The number of records resulting from a join depends on the join type and the cardinality of the join keys. An INNER JOIN returns m records (where m is the count of matching key combinations). A LEFT…
Self-introduction including current role, projects, and key responsibilities. Focus on SQL expertise, Python skills, and experience in data warehousing and modeling.
**Situation**: Context of the challenge. **Task**: Your responsibility. **Action**: Specific steps, tools, collaboration. **Result**: Quantified outcome. Structure: 1–2 min. "I'm a data engineer with X years focused on SQL, Python, and data warehousing. I've built pipelines processing [volume] for [domain]. Key responsibilities: designing schemas, optimizing ETL, ensuring data quality. Recent project: [brief example—e.g., migrated warehouse to Snowflake, 50% query improvement]....
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Frequently Asked Questions About EY Data Engineering Interviews
What topics are asked most frequently in EY data engineering interviews?▼
Technical interviews at EY heavily emphasize SQL and Cloud/Tools, accounting for the majority of technical questions in our repository of 24+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do EY interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at EY 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 EY?▼
The EY 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 EY data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the EY 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.