Globant Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Globant with 13+ 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 Globant
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 Globant Question Vault (10 Questions)
Showing 10 of 10Explain Dynamic Partition Pruning error and how to fix it.
**Architectural Logic**: DPP in Spark pushes dimension filters to fact table to skip partitions—type mismatch or missing broadcast breaks it. **Mechanism**: When dimension is filtered and broadcast, Spark pushes partition filter to fact. **Error Causes**: Partition column type mismatch (e.g., INT vs STRING); filter not pushed; broadcast hint missing. **Fix**: (1) Align join key types with partition column. (2) `broadcast(dim_df)` for small dimension....
How do you convert 3 rows into one column in SQL?
**STRING_AGG** (PostgreSQL): SELECT STRING_AGG(col, ', ' ORDER BY id) FROM (SELECT col FROM t LIMIT 3) sub. **LISTAGG** (Oracle), **GROUP_CONCAT** (MySQL), **ARRAY_AGG** (returns array). For exactly 3 rows: LIMIT 3 in subquery. **Determinism**: ORDER BY in agg....
How do you count occurrences in a column in SQL?
Group by the column and count. That gives one row per distinct value with its frequency. The COUNT variants differ, and the difference is the interview question COUNT( ) counts rows, including rows…
How do you keep a specific column on top in SQL?
To keep a specific column "on top" in SQL, you explicitly list it first in your SELECT statement. This action directly dictates the logical order of columns in the result set returned by the query.…
Explain read and write modes in Spark.
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark read modes: spark.read loads data (default overwrites for file sources). Write modes: (1) overwrite—replaces target; (2) append—adds data; (3) ignore—no-op if exists; (4) errorIfExists—fail if exists. Example: df.write.mode('append').parquet(path). For Delta: overwriteSchema; replaceWhere for conditional overwrite....
How do you convert an array column to multiple columns in PySpark?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Array to columns: from pyspark.sql.functions import col; df.select(col('array_col')[0].alias('col1'), col('array_col')[1].alias('col2')) or df.select(explode('array_col')).groupBy('id').pivot('array_col')....
How does Adaptive Query Execution (AQE) work?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Adaptive Query Execution (AQE) optimizes Spark queries at runtime. Introduced in Spark 3.x, it: (1) Coalesces partitions after shuffle based on actual data sizes (`spark.sql.adaptive.coalescePartitions.enabled`). (2) Converts Sort-Merge Join to Broadcast Join when runtime stats show a join side is small....
What is a serializer in Spark?
**Serializer**: Converts objects to bytes for shuffle, persistence, broadcast. Default: Java (slow). Kryo: `spark.serializer=org.apache.spark.serializer.KryoSerializer`. **Why Kryo**: 10x faster, smaller payload. Less network and memory. **Trade-off**: Register custom classes for Kryo efficiency: `spark.kryo.classesToRegister`. Unregistered = slower. **Scalability Trade-offs**: Kryo reduces shuffle size; faster jobs....
What is the difference between MapReduce and Spark?
**MapReduce**: Disk-based. Each job writes to HDFS between map and reduce. Multi-stage = multiple disk I/O. Latency: minutes to hours per stage. **Spark**: In-memory (when cached). DAG with lazy evaluation. RDD/DataFrame caching. 10–100x faster for iterative (ML, graph). **Why Spark Won**: Iterative algorithms (e.g., PageRank, K-means) run 10–100x faster. Unified batch + streaming. DataFrame API + Catalyst. **Scalability Trade-offs**: Spark needs more memory....
What is the difference between head() and take() in PySpark?
**head(n)**: Returns first n rows as list of Row. head() without arg = first row. DataFrame API. **take(n)**: Same—list of Row. RDD origin. Both are actions. **Functionally**: Equivalent for DataFrames. Use either for small samples. **Why Care**: Both bring data to driver. On huge DF, use `limit(n)` in transformation to reduce data before action. Or `df.limit(100).collect()` for sampling. **Scalability Trade-offs**: head(1000) on 1B rows = driver gets 1000; full scan....
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Frequently Asked Questions About Globant Data Engineering Interviews
What topics are asked most frequently in Globant data engineering interviews?▼
Technical interviews at Globant heavily emphasize Spark/Big Data and SQL, accounting for the majority of technical questions in our repository of 13+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do Globant interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Globant 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 Globant?▼
The Globant 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 Globant data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Globant 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.