Myntra Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Myntra with 12+ 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 Myntra
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 Myntra Question Vault (9 Questions)
Showing 9 of 9Write a Python program to calculate total spending, identify top 5 users by spending, and find the most purchased product
**Pandas:** total = df['amount'].sum(); top_users = df.groupby('user_id')['amount'].sum().nlargest(5); most_purchased = df['product_id'].value_counts().index[0]. **SQL:** SUM, GROUP BY + ORDER BY + LIMIT. For top-5: ROW_NUMBER() or RANK() OVER (PARTITION BY 1 ORDER BY total DESC). **Scalability:** For Myntra-scale: run in warehouse (Snowflake/BigQuery)—push compute....
Advanced SQL with CTEs and Conditional Joins
**CTEs:** WITH cte AS (...) SELECT ... FROM cte. Readability, reuse, recursion. **Conditional join:** AND in ON, or CASE in ON. Multiple LEFT JOINs with different conditions. WITH ranked AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY date DESC) rn FROM events ) SELECT r.*, u.name FROM ranked r LEFT JOIN users u ON r.user_id = u.id AND u.status = 'active' WHERE r.rn = 1;
Fact vs Dimension Table - categorize data
**Fact tables**: Measures (quantity, amount); granular events; append-only; FKs to dimensions; high row count. **Dimension tables**: Descriptors (product name, customer); lower row count; used for grouping/filtering. **Why separate**: Facts grow linearly; dimensions change slowly. Star schema optimizes analytics—filter/group by dimension, aggregate facts. **Scalability**: Facts partitioned by date; dimensions often fit in memory for broadcast....
HAVING vs WHERE - explain
**WHERE**: Filters rows before GROUP BY; cannot use aggregates. **HAVING**: Filters after GROUP BY; can use aggregates. Example: SELECT dept, AVG(salary) FROM employees WHERE hire_date > '2020' GROUP BY dept HAVING AVG(salary) > 50K. WHERE reduces input; HAVING filters groups....
SELF JOIN Applications - scenarios like manager-employee relationships
Self-join: table joined to itself. Manager-employee: SELECT e.name AS employee, m.name AS manager FROM emp e LEFT JOIN emp m ON e.manager_id = m.emp_id. Other uses: compare rows (find duplicates), hierarchies (org chart), sequences (consecutive dates). Alias both instances. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....
Slowly Changing Dimension (SCD) Type 4 - describe hybrid approach
SCD Type 4: hybrid—current in main dim, history in separate table. Main table: one row per key (Type 1 style). History table: all changes (Type 2 style). Query current: main table. Query history: history table. Balances performance (current) with full audit (history). Implement: main table overwrite; history table append on change. **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead....
Solve medium-level SQL questions involving two tables with null values - LEFT JOIN, RIGHT JOIN, INNER JOIN
With nulls in join keys: INNER excludes null keys (no match). LEFT: keeps left rows, null for right when no match or when key is null. RIGHT: mirror of LEFT. Test: create small tables with 0, NULL, 1; run each join; observe. Use COALESCE in ON to normalize: ON COALESCE(a.key,-1)=COALESCE(b.key,-1). **Why it matters**: Design choices compound at scale—wrong approach can cause 100× overhead. **Scalability trade-offs**: Profile before optimizing; validate on sample then full....
Write a SQL query leveraging window functions and timestamps to identify updates over time
SELECT id, value, LAG(value) OVER (ORDER BY timestamp) AS prev_value, value - LAG(value) OVER (ORDER BY timestamp) AS delta FROM table ORDER BY timestamp. For change detection: SELECT *, LAG(column) OVER (PARTITION BY id ORDER BY updated_at) AS prev, CASE WHEN column != LAG(column) OVER (...) THEN 'changed' END AS change_flag. **Why**: LAG gives previous row; compare to detect changes....
Delta vs Parquet - explain
**Why distinction matters**: Immutable vs mutable; different operational model. **Parquet**: Columnar format; immutable; no built-in transactions. Append = new files. **Delta**: Parquet data files + JSON transaction log. Adds ACID, time travel, schema evolution, upsert/merge. **Scalability trade-offs**: Parquet = simple, append-only; Delta = mutable, more features. **Cost implications**: Delta = log storage overhead; Parquet = just files....
Stop Guessing If Your Answers Clear the Bar at Myntra
DataEngPrep’s AI evaluates your real answers against strict FAANG hire rubrics. Check accuracy, optimize shuffle partitions, detect edge case omissions, and receive an instant 9/10 rewrite before stepping into your real interview loop.
Similar Tier Companies Hiring Data Engineers
Explore interview loops with overlapping system design and SQL standards.
Frequently Asked Questions About Myntra Data Engineering Interviews
What topics are asked most frequently in Myntra data engineering interviews?▼
Technical interviews at Myntra heavily emphasize SQL and Spark/Big Data, accounting for the majority of technical questions in our repository of 12+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do Myntra interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Myntra 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 Myntra?▼
The Myntra 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 Myntra data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Myntra 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.