Pubmatic Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Pubmatic 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 Pubmatic
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 Pubmatic Question Vault (10 Questions)
Showing 10 of 10Combine records by name with concatenated course values
To combine records by name with concatenated course values, use an aggregate string function along with a GROUP BY clause on the name column. This technique is commonly used for denormalization,…
Sort and merge arrays
**Why Merge Sorted Arrays:** Mergesort's merge step, merge K sorted streams (logs from multiple sources), and combining partitioned data. **Two Arrays:** Two pointers, O(m+n). K arrays: (1) Merge pairwise—O(N log K). (2) Heap of (value, stream_id)—O(N log K). Python: heapq.merge(a, b, c)—lazy, memory-efficient for large streams. **Scalability:** heapq.merge doesn't load all into memory—streams. For Spark: merge sorted part files via sortMerge join....
Count records for INNER JOIN and LEFT JOIN
**Architectural Logic**: Join type determines output cardinality and null semantics. **INNER JOIN**: Only matching rows from both tables; count ≤ min(left, right) when 1:1 or N:1. **LEFT JOIN**: All left rows; right columns NULL where no match; count = left count (if left is 'one' side). **Cardinality Rules**: INNER can reduce rows (excludes orphans); LEFT preserves left. Use COUNT(*) for total rows; COUNT(right.key) for non-null matches (LEFT JOIN gives matched count)....
Create partitioned table
**Architectural Logic**: Partitioning enables partition pruning—critical for cost and performance at scale. **BigQuery**: `CREATE TABLE dataset.sales (...) PARTITION BY sale_date` or `PARTITION BY DATE(created_at)`. **Snowflake**: `PARTITION BY (sale_date)` or expression. **Hive/Spark**: `PARTITIONED BY (sale_date DATE)`. **Why Partition**: Predicate on partition column skips non-matching partitions; 90% cost reduction on date-filtered queries....
Find average salary for each manager – Assume a table with manager_id and employee_salary
To find the average salary for each manager, you should use the AVG() aggregate function combined with a GROUP BY clause on the manager id . This groups all employees under the same manager and then…
Find non-common records in two tables (SQL EXCEPT or NOT IN)
EXCEPT: SELECT col FROM t1 EXCEPT SELECT col FROM t2 (A minus B). NOT IN: SELECT * FROM t1 WHERE col NOT IN (SELECT col FROM t2)—**NULL trap**: if t2 has NULL, NOT IN returns nothing. **Safe**: NOT EXISTS (SELECT 1 FROM t2 WHERE t2.col = t1.col). **EXCEPT** eliminates duplicates; NOT EXISTS doesn't....
Print only the newest record for each name – Use SQL Window functions (ROW_NUMBER, RANK, etc.)
Newest record per name: SELECT * FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY name ORDER BY created_at DESC) rn FROM t) t2 WHERE rn=1. Or RANK if ties should all be kept. DENSE_RANK for consecutive ranks. Use FIRST_VALUE/LAST_VALUE for single column: SELECT name, FIRST_VALUE(col) OVER (PARTITION BY name ORDER BY ts DESC) FROM t; then GROUP BY or DISTINCT. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....
Basic Spark commands – Create RDD, Load data, Filter
**Why syntax + semantics matter**: RDD vs DataFrame choice affects optimization; load options impact parallelism. **Commands**: Create RDD: `sc.parallelize([1,2,3])` or `sc.textFile("path")`. Load: `textFile`, `wholeTextFiles`, `sequenceFile` (RDD); `spark.read.csv()`, `spark.read.parquet()` (DataFrame). Filter: `rdd.filter(lambda x: x > 0)`; `df.filter(col("status") == "active")`....
Load data into Hive table from HDFS or local
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Load data into Hive: From HDFS: `LOAD DATA INPATH 'hdfs://path' INTO TABLE t` or `OVERWRITE`. From local: `LOAD DATA LOCAL INPATH '/local/path'`. For external: `CREATE EXTERNAL TABLE t (...) LOCATION 'hdfs://path'`. Insert from another table: `INSERT INTO t SELECT * FROM s`....
Read CSV, filter, and write to table using PySpark
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Read CSV, filter, write: `df = spark.read.option('header',True).csv('path'); filtered = df.filter(col('status')=='active'); filtered.write.mode('overwrite').saveAsTable('target_table')`. Or `.parquet('path')`....
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Frequently Asked Questions About Pubmatic Data Engineering Interviews
What topics are asked most frequently in Pubmatic data engineering interviews?▼
Technical interviews at Pubmatic heavily emphasize SQL and Spark/Big Data, 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 Pubmatic interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Pubmatic 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 Pubmatic?▼
The Pubmatic 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 Pubmatic data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Pubmatic 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.