TCS Data Engineer Interview Questions (2026)
Prepare for your technical rounds at TCS with 44+ 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 TCS
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 TCS Question Vault (41 Questions)
Showing 15 of 41How do you handle version conflicts for libraries?
Situation: Our dbt project and a shared Spark job both depended on different PyArrow versions; one upgrade broke the other. We had 3 similar conflicts in a quarter. Task: Stabilize dependencies and prevent future conflicts. Action: I enforced lock files (poetry.lock, pip-tools) and hashed requirements.txt for reproducible builds. I created an internal package index with vetted, tested versions. I introduced a dependency review process—any upgrade required CI to pass for all consumers....
How is Azure Key Vault used to manage encryption keys in Databricks?
Architectural flow: Key Vault stores secrets and keys. Databricks accesses via Azure AD (Managed Identity or SP). Use: DB passwords, API keys; dbutils.secrets.get(scope, key). Secret scope linked to Key Vault. CMK for workspace encryption....
What are the differences between %run and dbutils.notebook.run?
**%run**: Executes notebook inline in the same Spark session. Variables and imports are shared. Child notebook can modify parent's state. Use for quick composition, shared setup (imports, config). **dbutils.notebook.run()**: Executes in separate context. No variable sharing. Returns last expression. Supports timeout and arguments. Use for job workflows, parallel runs, parameterized execution. **When to use which**: %run for development, ad-hoc composition....
Can you describe the role of user groups in setting up these policies?
**Groups in RBAC**: Assign permissions to groups; add users to groups. Simplifies management—add user to analyst group vs. granting per-object. **Databricks**: Create group; grant on schema/table. Sync from IdP (Okta, Azure AD)....
How do you ensure version control when migrating notebooks?
**Approach**: (1) Export to .py or .ipynb; (2) Git—commit notebooks; use nbdime for diff; (3) Review—notebooks are JSON; (4) CI—papermill, nbval for tests; (5) No secrets—templates, inject at runtime. Pair notebooks with .py modules. Databricks Repos for sync....
How do you handle passing parameters between notebooks?
Parameters can be passed between notebooks using environment specific utilities like Databricks widgets, programmatic execution tools like Papermill, or external orchestrators such as Airflow. For…
How do you identify resource bottlenecks in cluster logs?
**Sources**: Spark UI (tasks, stages, shuffle); executor logs (memory, GC, CPU); driver logs; iostat, top. **Signals**: Skew (one task 10x slower); memory (spill, OOM); network (shuffle); stragglers. Tools: Spark UI, Prometheus....
How does cluster size impact parallelism limits?
Cluster size directly impacts the maximum degree of parallelism by determining the total computational resources available. More worker nodes and cores per worker generally allow for more tasks to…
WAQ for Desired Output (Age Group Count)
SELECT CASE WHEN age BETWEEN 0 AND 18 THEN '0-18' WHEN age BETWEEN 19 AND 35 THEN '19-35' WHEN age BETWEEN 36 AND 50 THEN '36-50' ELSE '51+' END AS age_group, COUNT(*) FROM people GROUP BY 1 ORDER BY 1. FLOOR(age/10)*10 for decades. Handle NULL; mutually exclusive buckets....
WAQ for Desired Output (Node Parent Relationship)
Recursive CTE for path: WITH RECURSIVE cte AS (SELECT id, parent_id, CAST(id AS VARCHAR) path FROM tree WHERE parent_id IS NULL UNION ALL SELECT t.id, t.parent_id, cte.path||'-'||t.id FROM tree t JOIN cte ON t.parent_id=cte.id) SELECT * FROM cte. Flat: SELECT node_id, parent_id ORDER BY parent_id NULLS FIRST....
What are the implications of enabling encryption at rest on storage performance?
Enabling encryption at rest on modern storage systems generally has a negligible impact on performance, often introducing less than 5% overhead, primarily due to hardware acceleration. For most cloud…
What are the security considerations for the control plane?
Control plane = resource management (create, delete, configure). Auth: MFA; minimal service accounts. Authorization: IAM least privilege; no broad admin. Audit: CloudTrail, immutable logs. Network: Private endpoints, VPC. Secrets: Rotate; secrets manager. Compliance: Encryption, retention. For EMR/Glue: Limit cluster create/delete; permission boundaries....
What role do workspace APIs play in this process?
Workspace APIs enable **programmatic environment management**—critical for CI/CD. **Why:** Manual setup causes drift; APIs = env-as-code. **Scalability:** Spin 100+ workspaces for parallel branch testing; tear-down prevents cost runaway. **Trade-offs:** Rate limits need backoff; creation is async—poll before deploy. **Cost:** Idle workspaces cost; use lifecycle policies. **Production:** Terraform + API for IaC; tokens in Vault; idempotent create/update for retries....
What strategies do you use to retry failed steps in workflows?
**Retry:** Exponential backoff + jitter. Libraries: tenacity (Python), resilience4j (Java). Max 3–5 retries; cap backoff. **When:** Transient only (5xx, timeouts); not 4xx. **Workflows (Airflow, Step Functions):** retry_delay, retries on task; DLQ for permanent failures. **Critical:** Idempotent ops; idempotency keys for APIs. **Cost:** Retries increase load—tune max; log for observability....
Can you give an example of processing nested JSON data using these functions?
Processing nested JSON data involves parsing the JSON string into a structured type, then accessing its elements. For arrays within the JSON, an explode operation is often necessary to flatten them…
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Frequently Asked Questions About TCS Data Engineering Interviews
What topics are asked most frequently in TCS data engineering interviews?▼
Technical interviews at TCS heavily emphasize Spark/Big Data and General/Other, accounting for the majority of technical questions in our repository of 44+ verified interview questions. Candidates should expect hands-on rounds testing query optimization, data skew mitigation, and end-to-end pipeline architecture.
How deep do TCS interviewers go on Apache Spark, SQL, and system design?▼
Interviewers at TCS 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 TCS?▼
The TCS 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 TCS data engineer interview questions with AI feedback?▼
You can practice directly on DataEngPrep.tech. Select any question from the TCS 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.