Datametica Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Datametica 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 Datametica
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 Datametica Question Vault (10 Questions)
Showing 10 of 10How do you drop columns with null values in PySpark?
Two distinct operations: (1) **Drop columns that are entirely null** (no non-null values): null_cols = [c for c in df.columns if df.filter(col(c).isNotNull()).count() == 0]; df = df.drop(*null_cols). **Caveat**: count() triggers a full scan—expensive on large tables. (2) **Drop rows with null in specified columns**: df.dropna(subset=["col1", "col2"]). **Scalability**: The column-null check is O(partitions × columns) and can be costly; consider sampling or inferring from schema/sample....
Discuss Primary, Foreign, and Composite Keys.
**PK**: Uniquely identifies row; NOT NULL + unique. Surrogate (auto-increment) vs. natural. Surrogate preferred for stability. **FK**: References PK in another table; referential integrity. Index FKs for join performance. **Composite**: Multiple columns for uniqueness—e.g., (order_id, line_item_id)....
How to optimize join of large and small tables in Spark?
Broadcast the small table. Spark sends a full copy to every executor so the large table is joined in place, which removes the shuffle on the expensive side entirely. Why this is so much faster A…
Discuss common transformations used in Spark code.
**Why transformations matter**: Choice affects Catalyst optimization and performance. **Common**: map, filter, select, withColumn, drop, groupBy, agg, join, union, distinct. Window: row_number, rank, sum over. **Scalability trade-offs**: Narrow (map, filter) = no shuffle; wide (groupBy, join) = shuffle. Prefer narrow; minimize wide. **Cost implications**: UDF breaks optimization; built-ins preferred....
Explain Delta Table features – Z-ordering and Time Travel.
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Z-ordering and Time Travel are core Delta Lake optimizations. Z-ordering physically colocates related data in the same files by ordering rows based on specified columns (e.g., date, customer_id), dramatically improving predicate pushdown and reducing I/O for range queries. Use OPTIMIZE table_name ZORDER BY (date, region)....
Explain Spark Architecture – Driver, Executors, and Tasks.
**Section 1 — The Context (The 'Why')** Spark's driver-executor architecture creates a single point of coordination: the driver builds the DAG and schedules tasks, while executors perform the actual work. Driver OOM from collect() or executor OOM from data skew are common production failures. A naive fix—increasing driver memory for an executor skew problem—wastes cost and does not solve the root cause....
Explain Spark's execution process – Job/Stage/Task creation.
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark execution: (1) Action triggers job; (2) DAG Scheduler splits the DAG into stages at wide transformation boundaries (shuffle dependency); (3) Each stage has tasks (one per partition); (4) Task Scheduler launches tasks on executors. Stage boundary occurs when a shuffle is required—e.g., before a reduceByKey or join....
GroupByKey vs ReduceByKey – Differences and performance implications?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. GroupByKey: shuffles all values per key; memory-heavy. ReduceByKey: combiner reduces before shuffle; more efficient. Example: ReduceByKey((a,b)=>a+b) vs GroupByKey then sum. Use ReduceByKey when aggregation is associative....
How to fill null values in PySpark?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Fill nulls in PySpark: (1) `fillna(value)` or `fillna(dict)` for specific columns: `df.fillna(0)` or `df.fillna({'col1':0,'col2':'unknown'})`. (2) `na.fill()`. (3) `when/otherwise`: `df.withColumn('col', when(col('col').isNull(), lit(0)).otherwise(col('col')))`. For forward/back fill: Use window functions....
How to remove duplicates in PySpark?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Remove duplicates in PySpark: (1) `df.dropDuplicates()`—all columns. (2) `df.dropDuplicates(['col1','col2'])`—subset. (3) `df.distinct()`—full row. (4) For deterministic 'first' row: `df.dropDuplicates(['key']).orderBy('timestamp', ascending=False)` with window....
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Frequently Asked Questions About Datametica Data Engineering Interviews
What topics are asked most frequently in Datametica data engineering interviews?▼
Technical interviews at Datametica 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 Datametica interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Datametica 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 Datametica?▼
The Datametica 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 Datametica data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Datametica 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.