Question 1

Tell me about a time when you faced a challenging situation at work and how you handled it.

Accepted Answer

Situation: During a major product launch, a critical data pipeline failed. Downstream dashboards showed incorrect KPIs; execs were escalating; the root cause was unclear. Task: Restore data integrity and stakeholder confidence while preventing recurrence. Action: I triaged using a runbook: checked Spark/Airflow logs, source schema, and recent deployments. I identified a breaking schema change in the upstream API that wasn't in our contract....

Question 2

Could you describe a specific cost optimization strategy you implemented in the cloud and its results?

Accepted Answer

Situation: BigQuery costs spiked 40% in a quarter; ad-hoc queries and unoptimized scans dominated spend. Task: Reduce spend without constraining analysts or degrading UX. Action: I led a cost optimization initiative: (1) Partitioned and clustered high-cardinality columns—event_date, user_id—to cut bytes scanned by ~50%; (2) Implemented slot reservations for predictable workloads; (3) Added query cost attribution via labels and alerts for queries exceeding $50; (4) Created materialized views for...

Question 3

How Airflow operates in a Kubernetes environment

Accepted Answer

Architectural logic: KubernetesExecutor runs each task in its own pod. Scheduler creates pod → task runs → pod terminates. Isolation and per-task resource control. DAGs in shared volume or Git-sync. Example: Helm deploys scheduler, workers, webserver; task runs spark-submit in pod....

Question 4

How Airflow stores logs and the role of its backend database

Accepted Answer

Architectural split: Logs = filesystem or remote (S3/GCS); default logs/dag_id/task_id/date/. Database = metadata (DAGs, runs, task instances, connections)—Postgres/MySQL. Logs not in DB. Best practice: Remote storage (S3/GCS) for logs; retention policy; backup DB; size for DAG volume.

Question 5

How did you contribute to cost optimization initiatives while working with cloud technologies?

Accepted Answer

Situation: Cloud spend grew with adoption. Task: Reduce cost without impacting delivery. Action: (1) Right-sized EC2 and EMR. (2) Reserved Instances for baseline. (3) S3 lifecycle and Intelligent-Tiering. (4) Stopped non-prod outside hours. (5) Tagging and chargeback. (6) Query optimization (Athena, Redshift). Result: ~30% cost reduction....

Question 6

Write code to upload Parquet files to an S3 bucket using boto3

Accepted Answer

**Simple**: s3.upload_file('data.parquet', 'my-bucket', 'path/data.parquet')—handles multipart for >8MB. **From DataFrame**: df.to_parquet('s3://bucket/path/', engine='pyarrow') with s3fs. **Programmatic**: pyarrow read_table, write to BytesIO, s3.put_object(Body=buffer.getvalue()). **Large files**: upload_file does multipart automatically; for >5GB, configure multipart threshold. **Encryption**: put_object(ServerSideEncryption='aws:kms')....

Question 7

Can you share an example of a project you worked on that had a significant impact on your organization?

Accepted Answer

**STAR**: **Situation**: 60% of reports had data quality issues. **Task**: Build data quality framework. **Action**: Implemented Great Expectations + dbt tests. Integrated in Airflow; automated alerts. **Result**: Errors dropped 80%; faster detection....

Question 8

Deadlock Prevention - how deadlocks occur and how to prevent them

Accepted Answer

**Prevention**: Acquire locks in consistent order. Use timeouts. Keep transactions short. Avoid holding locks during I/O. **Detection**: Lock wait graphs. **Resolution**: Kill one transaction. **Best practice**: Design for no circular wait; document lock order.

Question 9

Designing Mixpanel - event-driven analytics platform

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in General/Other 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....

Question 10

How did you develop the Datahub using Open Source Projects such as Spline & Datahub?

Accepted Answer

**Components**: Datahub (metadata catalog, lineage, search); Spline (Spark lineage). Spline agents in Spark jobs emit lineage to Spline server; Datahub ingests via API/connectors. Enrich with glossary, ownership, tags. Deploy via Docker/K8s....

Walmart Data Engineer Interview Questions

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Walmart Data Engineer Interview Questions

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