HashedIn Data Engineer Interview Questions (2026)
Prepare for your technical rounds at HashedIn with 18+ 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 HashedIn
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 HashedIn Question Vault (15 Questions)
Showing 15 of 15Walk through the three AQE features in Spark 3.x (coalesce, join switch, skew join)—how they operate at shuffle boundaries, which configs enable them, and what happens when AQE cannot help.
AQE in Spark 3.x performs runtime reoptimization at shuffle boundaries. Three features: (1) Coalesce shuffle partitions (spark.sql.adaptive.coalescePartitions.enabled): post-shuffle, merge undersized partitions into fewer tasks—reduces scheduler overhead and small-task waste. (2) Join strategy switch: if runtime stats show one side small, convert sort-merge to broadcast—eliminates shuffle for that side....
What is Adaptive Query Execution (AQE) in Spark 3.x, and how does it improve performance?
AQE re-optimizes at runtime using actual statistics at stage boundaries, addressing the planning-time blind spot (e.g., wrong size estimates, skew). **Three features**: (1) **Coalesce shuffle partitions**—merges small partitions after shuffle to reduce task overhead; avoids 10K tiny tasks. (2) **Switch join strategy**—if one side is smaller than expected, converts sort-merge to broadcast; avoids unnecessary shuffle....
Identify who is a manager and who is not.
Manager = employee who is parent_id for at least one other. SQL: SELECT e.id, e.name, CASE WHEN EXISTS (SELECT 1 FROM employees e2 WHERE e2.manager_id = e.id) THEN 'Manager' ELSE 'Non-Manager' END AS role FROM employees e. WHY: EXISTS is efficient—short-circuits; avoids duplicate scans. Handle NULL manager_id (CEO). SCALABILITY: Index on manager_id; for large orgs, consider materialized view or closure table for hierarchy queries....
Check if a number is prime.
**Logic:** Prime if no divisor in [2, √n]. **Code:** `for i in range(2, int(n**0.5)+1): if n%i==0: return False`. O(√n). **Optimize:** Check 2, then odds. **Large n:** Miller-Rabin probabilistic. **Why:** Validation, sampling....
Implement a function to find the maximum sum subarray (Kadane's algorithm).
Kadane's algorithm efficiently finds the maximum sum of a contiguous subarray within a one dimensional array of numbers. It achieves this with optimal O(n) time complexity and O(1) space complexity.…
Implement a function to reverse a string without using built-in methods.
To reverse a string without using built in methods, the most common approaches are iteratively prepending characters to a new string or using a two pointer technique to swap characters in an…
Add a new column with manager names for each employee using a self-join.
To add a new column with manager names for each employee, you perform a self join on the employees table, linking each employee's manager id to another instance of the table representing their…
Add a new column with the average salary by department.
To add a new column with the average salary by department, use a SQL window function with AVG() and PARTITION BY . This calculates the average salary for each department while preserving all…
Duplicate characters in a string (e.g., '123a!' to '112233aa!!').
**Architectural Logic**: Character duplication is a string transform; SQL capabilities vary. **PostgreSQL**: `SELECT regexp_replace('123a!', '(.)', '\1\1', 'g')`. **Spark**: `regexp_replace(col("str"), "(.)", "$1$1")`. **Python**: `''.join(c*2 for c in s)`. **Why Choose**: SQL regex works in many dialects; application layer clearer for complex manipulation. **Scalability**: For bulk, prefer batch processing in Spark/Python; SQL for inline transforms....
How do you design a scalable and fault-tolerant data warehouse on a cloud platform?
**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 the differences between Spark's shuffle and broadcast join. When would you use each?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Shuffle join: both sides distributed by key; SortMergeJoin or HashJoin; large data movement. Broadcast join: small table sent to all executors; no shuffle for large table; efficient when one side is small (under 100MB). Use broadcast when one table fits in memory: df1.join(broadcast(df2)). Use shuffle when both large....
How do you monitor and debug Spark applications in production?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Production Spark monitoring: Spark UI/History; application logs; metrics (executor failures, GC); alerting on failures and SLA. Debug: check failed task logs; identify skew; validate data....
How would you optimize a Spark job that takes too long to run in production?
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Optimize slow production job: (1) Identify bottleneck—Spark UI, stage duration, skew. (2) Fix skew—salting, split hot keys. (3) Tune resources—executor memory/cores, parallelism. (4) Optimize logic—remove unnecessary shuffles, use broadcast. (5) Data—partition pruning, file compaction. (6) Consider incremental processing....
What are the steps to efficiently process 1 TB of data in Spark?
**Partitioning**: Input partitioned (e.g., date); repartition to 2–4x cores (e.g., 128–256 for 64 cores). **Format**: Parquet/ORC; columnar, predicate pushdown, compression. **Cluster**: 64–128 cores; executors 4–8 cores, 8–16GB....
Design a Data Warehouse for an e-commerce platform.
**Section 1 — The Context (The 'Why')** This system faces scale and failure challenges at production. A naive approach breaks under load, loses data, or violates compliance. The primary challenge varies by domain: notifications need preference respect; banking needs ACID; pipelines need idempotency....
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Frequently Asked Questions About HashedIn Data Engineering Interviews
What topics are asked most frequently in HashedIn data engineering interviews?▼
Technical interviews at HashedIn heavily emphasize Spark/Big Data and Python/Coding, accounting for the majority of technical questions in our repository of 18+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do HashedIn interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at HashedIn 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 HashedIn?▼
The HashedIn 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 HashedIn data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the HashedIn 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.