Tech Mahindra Data Engineer Interview Questions (2026)
Prepare for your technical rounds at Tech Mahindra with 22+ 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 Tech Mahindra
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 Tech Mahindra Question Vault (19 Questions)
Showing 15 of 19What are the key components of AWS Glue, and how do they work together?
Glue Catalog: Central metadata store (Hive-compatible)—enables querying S3 data via Athena/Redshift Spectrum without moving it. Glue Crawlers: Schema discovery and Catalog population—useful for ad-hoc sources; at scale, prefer schema-as-code to avoid crawler cost and drift. Glue ETL Jobs: Serverless Spark for transforms; auto-scaling, pay-per-DPU. Glue DataBrew: Visual prep for non-engineers. Glue Schema Registry: Schema evolution for streaming (Kafka, Kinesis)....
What is Azure Data Factory (ADF), and what are its main components?
ADF is a cloud-native data integration service for orchestration and movement. Components: Pipelines (logical groups of activities), Activities (Copy, Lookup, Databricks, Data Flow), Datasets (structure definitions), Linked Services (connection configs), Triggers (schedule or event-based), Integration Runtime (IR—compute for execution). Flow: Linked Service -> Dataset -> Activity -> Pipeline -> Trigger....
What is the difference between S3 and HDFS?
S3: Object storage—durable, highly available, decoupled from compute. Pay-per-use, virtually unlimited scale. Eventual consistency (now strong for new overwrites). No data locality. HDFS: Distributed file system—block-based, colocated with compute nodes. Data locality reduces network I/O; strong consistency. Requires cluster management. Why it matters: S3 enables cloud-native, serverless patterns (Lambda, Glue, Athena); HDFS optimizes for batch processing where locality matters....
What is the role of AWS Lambda in a data engineering pipeline?
Lambda is serverless compute for event-driven, short-lived tasks. Roles: (1) Orchestration: Trigger Glue, EMR, or Step Functions when files land (S3), events arrive (Kinesis), or schedules fire. (2) Light transforms: JSON→Parquet, small aggregations, API calls. (3) Alerts: Validate data, send Slack/PagerDuty on anomalies. Why: No server management; scale to thousands of concurrent invocations; pay per request and duration. Limitations: 15-min timeout, 10GB memory, 6MB payload (sync)....
What is the role of the Integration Runtime (IR) in ADF?
IR is the compute layer that executes ADF activities—data movement, transformations, and external calls. Types: (1) Azure IR: Managed, cloud-native—for cloud-to-cloud copies, Data Flows, Databricks calls. No maintenance. (2) Self-hosted IR: Install on your VM or container—for on-prem, VNet, or private data sources. You manage scaling and uptime. (3) Azure-SSIS IR: For running SSIS packages in Azure. Why it matters: Data never flows through ADF; it flows through IR. Network topology (cloud vs....
Detail examples of inner, outer, left, and right joins.
**Architectural Logic**: Join choice directly impacts query cost, data correctness, and downstream semantics. INNER keeps only matching rows—ideal when referential integrity is enforced and you want to exclude orphans; minimizes shuffle and output size. LEFT preserves all from A with optional B—use when B is a "dimension" that may not exist (e.g., optional user attributes); RIGHT is mirror of LEFT; FULL OUTER preserves both sides—expensive due to bilateral NULL expansion....
Difference Between Internal and External Tables in BigQuery
**Architectural Logic**: Internal (native) tables: BigQuery owns metadata and storage (Colossus). Columnar layout, partitioning, clustering, automatic optimization. Dropping = data deleted. External tables: BigQuery owns metadata only; data lives in GCS, Drive, Bigtable. Dropping = metadata deleted, data persists. **Why**: Internal enables DML, streaming inserts, partitioning strategies, cost-effective storage classes....
Provide Data Pipeline for GCP Data Engineering
**Architecture**: Medallion or layered: Raw (Cloud Storage) → Curved (Dataflow/Dataproc) → Curated (BigQuery). **Ingest**: Pub/Sub for streaming; Cloud Storage for batch (Scheduled transfer, gsutil). **Processing**: Dataflow (Apache Beam) for streaming and batch—unified API. Dataproc for Spark when you need MLlib or custom libs. **Orchestration**: Cloud Composer (managed Airflow). **Storage/Analytics**: BigQuery for SQL; GCS for object storage....
What is XCom in Airflow?
**XCom**: Cross-communication—tasks pass small data (e.g., file path, ID). Push via return value or xcom_push(); pull via xcom_pull(). Stored in metadata DB. **Limitation**: Not for large data—metadata DB is not a store. Typical limit ~1 KB per value (configurable but not recommended to increase). **Use**: Pass S3 paths, query IDs, status codes. **Best practice**: Pass references, not payloads. For large data, use external storage (S3, DB) and pass reference....
Are You Aware of Beam?
**Apache Beam**: Unified batch and stream API. Write once; run on Flink, Spark, Dataflow. **Concepts**: PCollection, PTransform, Pipeline. **GCP Dataflow**: Native runner. **Why**: Portable ETL across runners. **Trade-off**: Abstraction has cost; runner-specific tuning limited....
What is PCollection?
A PCollection (Parallel Collection) in Apache Beam is an immutable, distributed dataset that represents a collection of elements of a specific type T . It is the fundamental data abstraction in Beam,…
What is ParDo and Map?
ParDo and Map are fundamental transformations in Apache Beam for processing data elements in parallel. Map is a specialized, simpler form of ParDo that performs a one to one transformation, while…
Python Code Using Constructors in a Class
**Why Constructors Matter:** __init__ establishes invariants (required fields, connections, config). In data pipelines, misconfigured objects cause late failures (e.g., missing DB connection at runtime). **Key Points:** __new__ creates the object; __init__ initializes it. Never use mutable defaults: def __init__(self, config={})—shared across instances. Use None and assign....
How to Use Dataflow with BigQuery
Dataflow, Google Cloud's managed service for Apache Beam, integrates seamlessly with BigQuery to perform complex, scalable data transformations, enrichment, and analysis, especially for use cases…
Query for 2nd Latest Joining Per Department
2nd latest joining per department: WITH ranked AS (SELECT *, DENSE_RANK() OVER (PARTITION BY dept ORDER BY join_date DESC) rn FROM employees) SELECT * FROM ranked WHERE rn=2. Use DENSE_RANK so ties get same rank; ROW_NUMBER gives unique rank. Alternative: NTH_VALUE or LAG. **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....
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Frequently Asked Questions About Tech Mahindra Data Engineering Interviews
What topics are asked most frequently in Tech Mahindra data engineering interviews?▼
Technical interviews at Tech Mahindra heavily emphasize Cloud/Tools and SQL, accounting for the majority of technical questions in our repository of 22+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do Tech Mahindra interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at Tech Mahindra 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 Tech Mahindra?▼
The Tech Mahindra 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 Tech Mahindra data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the Tech Mahindra 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.