Yash Technologies Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Yash Technologies with 23+ 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 Yash Technologies
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 Yash Technologies Question Vault (20 Questions)
Showing 15 of 20Discuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.
**ROW_NUMBER()**: Unique sequential numbers (1, 2, 3...); no ties—deterministic only with ORDER BY uniqueness. **RANK()**: Same rank for ties; skips (1, 2, 2, 4). **DENSE_RANK()**: Same rank for ties; no gaps (1, 2, 2, 3). **Project examples**: ROW_NUMBER() to deduplicate events by (user_id, event_time) keeping first—critical when upstream sends duplicates. DENSE_RANK() for 'top 10 products per category' reports—avoids gaps when filtering....
Briefly introduce yourself and walk us through your journey as a Data Engineer so far.
I began my career as a software engineer, evolving into a Staff Data Engineer focused on building robust, scalable, and trustworthy data platforms. My journey has been driven by a passion for…
Can you explain the difference between OLTP and OLAP?
OLTP (Online Transaction Processing) systems are optimized for many concurrent, small, atomic transactions (inserts, updates, deletes), ensuring data integrity and consistency. OLAP (Online Analytical…
Describe a time when you had to optimize a slow SQL query. What steps did you take?
**Situation**: A critical exec report was timing out at 30+ minutes; SLA was 5 minutes. **Task**: Diagnose and fix without changing business logic. **Action**: I ran EXPLAIN (ANALYZE) and found: (1) missing index on join key causing full table scan, (2) cross join that could be inner join with a filter, (3) filter in HAVING that could move to WHERE to reduce rows early, (4) unnecessary ORDER BY on a subquery....
Explain the concept of ACID properties in the context of databases.
ACID properties (Atomicity, Consistency, Isolation, Durability) are a set of principles that guarantee reliable transaction processing in databases, ensuring data integrity and reliability even amidst…
Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
INNER JOIN returns only rows with matches in both tables. LEFT JOIN returns all rows from the left table and matching rows from the right, with NULLs for non matches. RIGHT JOIN is symmetrical,…
How do you handle NULL values in SQL? Mention functions like COALESCE and NULLIF.
Handling NULL values in SQL is critical for data integrity and accurate query results, involving explicit filtering, substitution, and conditional assignment due to their unique behavior in…
What is a Common Table Expression (CTE), and when would you use it?
A Common Table Expression (CTE) is a named, temporary result set defined within a WITH clause, which can then be referenced multiple times within the main SQL query. It acts like a temporary view that…
What is the difference between a primary key and a unique key?
A primary key uniquely identifies each row in a table, disallows NULL values, and there can only be one per table. A unique key also enforces uniqueness across specified columns but allows a single…
What is the difference between WHERE and HAVING clauses in SQL?
The WHERE and HAVING clauses both filter data in SQL, but they operate at different stages of query execution and on different types of data. WHERE filters individual rows before any grouping or…
How do you ensure smooth communication between data scientists, business teams, and developers?
Situation: At scale, misalignment between data science (model outputs), business (metrics definitions), and engineering (SLA constraints) causes rework and friction. Task: Establish a communication model that reduces ambiguity while keeping delivery velocity high. Action: I implement formal data contracts (schema-first, versioned APIs) as the source of truth. I run a Data Council—weekly forum with reps from each team—to prioritize work by business impact (ICE score) and resolve dependencies....
SQL query to find the second highest salary from each department.
The most robust SQL approach to find the second highest salary from each department utilizes a window function, specifically DENSE RANK() , to assign a rank within each department based on salary,…
Triggers in ADF, especially tumbling window triggers.
**Architectural Logic:** ADF triggers orchestrate pipeline runs; tumbling window triggers partition time into fixed, non-overlapping intervals (e.g., 15min, 1hr). **Why tumbling over schedule:** Idempotency—each window processes mutually exclusive data, enabling safe retries and backfills. **Scalability:** Max concurrency controls parallel window execution; oversubscription can exhaust DTU/IR capacity. **Cost:** Higher concurrency = more runs = higher IR cost; tune window size vs. SLA....
Explain strategies for managing schema changes in PySpark over time.
Schema evolution in PySpark is architecturally driven by two competing forces: storage economics (rewriting entire datasets is costly) and query correctness (downstream consumers break when schemas shift). **Why it matters**: At petabyte scale, a full rewrite for a new column can cost thousands in compute and hours of downtime. **Strategies with trade-offs**: (1) mergeSchema—additive only, zero rewrite cost, but schema drift accumulates; use for append-heavy pipelines....
Explain the benefits of using DataFrames over RDDs.
DataFrames vs RDDs is a design trade-off between optimization surface and control. **Why DataFrames win for most workloads**: Catalyst applies predicate pushdown, projection pruning, and join reordering—optimizations impossible on opaque RDDs. Tungsten uses columnar in-memory layout and whole-stage codegen, yielding 5–10x speedups on analytical workloads....
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Frequently Asked Questions About Yash Technologies Data Engineering Interviews
What topics are asked most frequently in Yash Technologies data engineering interviews?▼
Technical interviews at Yash Technologies heavily emphasize SQL and Spark/Big Data, accounting for the majority of technical questions in our repository of 23+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do Yash Technologies interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Yash Technologies 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 Yash Technologies?▼
The Yash Technologies 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 Yash Technologies data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Yash Technologies 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.