Hexaware Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Hexaware 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 Hexaware
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 Hexaware Question Vault (10 Questions)
Showing 10 of 10How do you copy all files from one source path to target in ADF?
In Azure Data Factory (ADF), you can copy all files from a source path to a target using primarily two methods: a single Copy Data activity with wildcards, or a combination of GetMetadata, ForEach,…
Explain Job vs. Interactive Clusters.
**Job Clusters**: Run single workload; terminate when done. Cost-effective for batch ETL, scheduled jobs. Spin up, run, shut down. **Interactive**: Stay alive for ad-hoc queries, notebooks. Used by analysts. Use auto-termination (e.g., 30 min idle) to control cost. **Cost**: Job = pay per run; interactive = pay for idle. Pool clusters for faster startup with many short jobs....
How do you increase job performance? What techniques and optimizations?
**Techniques**: (1) Partitioning—by filter columns; coalesce/repartition; (2) Predicate pushdown; partition pruning; (3) Broadcast small tables; (4) Cache reused DataFrames; (5) Right-size executors; parallelism; (6) Avoid UDFs; native functions; (7) Parquet, columnar; compaction....
Explain how to implement cumulative sum in SQL.
**Architectural Logic**: Window function with unbounded preceding for running total. **Implementation**: `SELECT date, amount, SUM(amount) OVER (ORDER BY date) AS cumulative_sum FROM sales`. Per group: `SUM(amount) OVER (PARTITION BY product_id ORDER BY date)`. **Frame**: Default ROWS UNBOUNDED PRECEDING. **Use**: Running balance; YTD. **Scalability**: Single pass; efficient. **Cost**: No extra scan. **Best Practice**: Deterministic ORDER BY (tie-breaker if needed); understand RANGE vs ROWS....
Discuss Delta Logs file format and its significance.
**Why Delta Log matters**: Enables ACID, time travel, schema evolution. **Format**: JSON files in `_delta_log/`; each transaction = new file. Records add/remove file actions, metadata. **Significance**: Single source of truth for table state; concurrent readers see consistent snapshot. **Scalability trade-offs**: Log grows; VACUUM removes old files beyond retention. **Cost implications**: Log = small storage; many small transactions = many small log files....
Explain SCD1 and SCD2 in Databricks PySpark with examples.
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. SCD1 (Slowly Changing Dimension Type 1) overwrites existing records with new data—no history. SCD2 maintains full history by adding new version rows. SCD1 in PySpark: df_target.join(df_source, 'id', 'left').select(coalesce(df_source.col, df_target.col))....
Explain aggregation functions in PySpark with examples and use cases.
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. PySpark aggregation functions: sum(), count(), avg(), min(), max(), collect_list(), collect_set(). Use with groupBy: df.groupBy('dept').agg(sum('salary'), avg('age')). Window functions: row_number(), rank(), dense_rank(), sum() over partition. Example: df.withColumn('rank', rank().over(Window.partitionBy('dept').orderBy(desc('salary'))))....
How do you access Delta Logs?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Delta logs: transaction log in _delta_log/ as JSON. Access: DESCRIBE HISTORY table_name; SELECT * FROM table_name VERSION AS OF n. For low-level: spark.read.json('path/_delta_log/*.json')....
How do you connect to Blob Storage in Databricks?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Databricks to Blob: (1) Mount—dbutils.fs.mount(source='wasbs://...', mount_point='/mnt/mymount'); (2) Service principal or SAS; (3) Access: spark.read.parquet('/mnt/mymount/path'). Or use spark.conf for account key....
What are the steps to connect to Salesforce?
**Steps**: (1) Create Connected App in Salesforce (Client ID, Secret). (2) OAuth 2.0 or Username-Password flow. (3) Use spark-salesforce connector or REST client. (4) Load: `spark.read.format("com.springml.spark.salesforce").option("username", ...).option("password", ...).option("soql", "SELECT ...").load()`. (5) Bulk API for large exports. **Why Care**: Salesforce has rate limits (API calls, concurrent connections)....
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Frequently Asked Questions About Hexaware Data Engineering Interviews
What topics are asked most frequently in Hexaware data engineering interviews?▼
Technical interviews at Hexaware heavily emphasize Spark/Big Data and General/Other, 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 Hexaware interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Hexaware 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 Hexaware?▼
The Hexaware 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 Hexaware data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Hexaware 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.