Real-World Data Engineering Debugging Scenarios (With Solutions) – 2026

 

Why Debugging Matters More Than Building Pipelines

In production systems, pipelines rarely fail cleanly.
More often, they succeed with incorrect data, which is far more dangerous.

Most senior Data Engineering interviews today include debugging scenarios, not just “how would you build X”.

Below are real situations Data Engineers face—and how to debug them correctly.

---

Scenario 1: Pipeline Succeeded, but Dashboard Numbers Are Wrong

Problem

A daily pipeline ran successfully, but:

• Revenue numbers are inflated

• User counts are higher than expected

• No job failures or alerts

Common Root Causes

• Duplicate ingestion

• Incorrect joins

• Missing deduplication

• Late-arriving data processed twice

How to Debug

1. Compare row counts between raw and transformed tables

2. Check if data for the same date was ingested more than once

3. Validate join keys (many-to-many joins are common culprits)

4. Check incremental logic (e.g., updated_at filters)

Example Fix

If duplicates exist:

SELECT id, COUNT(*)
FROM orders
GROUP BY id
HAVING COUNT(*) > 1;

Then fix by:

• Deduplicating using ROW_NUMBER()

• Adding idempotency keys

• Fixing incremental filters

---

Scenario 2: Job Works in Dev but Fails in Production

Problem

• Pipeline runs fine on small datasets

• Fails or times out in production

• Memory errors or executor failures appear

Common Root Causes

• Data skew

• Large shuffles

• Poor partitioning

• Cartesian joins

How to Debug

1. Check data distribution (look for skewed keys)

2. Identify joins on high-cardinality columns

3. Review execution plan

4. Validate partitioning strategy

Example Fix

Instead of:

JOIN large_table ON user_id

Use:

• Pre-aggregation

• Bucketing

• Broadcast joins (when applicable)

• Repartitioning on correct keys

---

Scenario 3: Incremental Pipeline Misses Data

Problem

• New records missing for certain dates

• Backfill required frequently

• No failures, but gaps exist

Common Root Causes

• Late-arriving data

• Incorrect watermark logic

• Timezone mismatches

How to Debug

1. Compare source system timestamps with pipeline filters

2. Check if >= vs > caused exclusion

3. Identify timezone conversions

4. Validate backfill logic

Example Fix

Instead of:

WHERE updated_at > last_run_time

Use:

WHERE updated_at >= last_run_time - INTERVAL '1 DAY'

And deduplicate downstream.

---

Scenario 4: Duplicate Records in Production Tables

Problem

• Duplicate rows appear after retries

• Manual cleanup required

• Happens only when failures occur

Common Root Causes

• Non-idempotent pipelines

• Retries without state management

• Missing unique constraints

How to Debug

1. Check retry behavior in orchestration

2. Identify whether writes are append-only

3. Validate primary or natural keys

Example Fix

• Use merge/upsert logic instead of inserts

• Add unique keys at transformation layer

• Make pipeline idempotent

---

Scenario 5: Scheduled Jobs Run Out of Order

Problem

• Downstream job runs before upstream completes

• Partial data processed

• Inconsistent outputs

Common Root Causes

• Incorrect DAG dependencies

• Manual reruns without clearing state

• Misconfigured schedules

How to Debug

1. Inspect task dependencies

2. Verify execution dates vs run dates

3. Check rerun/backfill behavior

Example Fix

• Enforce strict upstream dependencies

• Avoid hardcoded dates

• Use logical execution dates consistently

This is commonly seen in tools like Apache Airflow.

---

Scenario 6: Streaming Pipeline Shows Data Lag

Problem

• Real-time dashboard lags by minutes or hours

• No errors reported

• Consumers appear healthy

Common Root Causes

• Consumer lag

• Slow processing logic

• Downstream bottlenecks

How to Debug

1. Monitor consumer offsets

2. Check processing time per batch

3. Identify slow transformations

4. Validate scaling configuration

Example Fix

• Increase parallelism

• Optimize transformations

• Add backpressure handling

• Scale consumers appropriately

---

Scenario 7: AI / GenAI System Produces Incorrect Results

Problem

• AI assistant gives outdated or incorrect answers

• Retrieval seems inconsistent

• No model errors

Common Root Causes

• Stale data in vector store

• Incorrect joins between data sources

• Partial data ingestion

How to Debug

1. Validate freshness of source data

2. Check embedding generation timing

3. Verify retrieval filters

4. Trace input data used for responses

Example Fix

• Enforce data freshness SLAs

• Rebuild embeddings on updates

• Add monitoring on data feeds

This is increasingly relevant in AI-enabled data systems.

---

How Interviewers Expect You to Answer Debugging Questions

Good answers:

• Start with data validation

• Narrow down the failure systematically

• Explain assumptions clearly

• Propose prevention, not just fixes

Bad answers:

• Jump directly to tools

• Guess without isolating root cause

• Blame infrastructure immediately