Prepare for your Databricks data engineering interview

Lakehouse architecture and pipeline design cases calibrated to Databricks’ Delta Lake-first, open-source data engineering culture.

Socratify AI

The Interview

What Databricks is looking for

Pipeline Design Interview

Data Pipeline Architecture

01Delta Lake Ingestion & ACID Transactions

02Exactly-Once Semantics Design

03Schema Evolution & Partition Strategy

04Lakehouse Performance Optimization

Platform Case Interview

Data Platform Diagnostics

01Spark Job Skew Root Cause Analysis

02Structured Streaming Lag Diagnosis

03Cluster Sizing & Cost Optimization

04Data Quality Framework Design

Behavioral Interview

01Data Reliability Ownership

02Cross-Team Pipeline Adoption

03Technical Complexity Communication

04Production Incident Learning

Delta Lake internals and exactly-once design//Spark skew and cluster optimization cases//Lakehouse architecture trade-off reasoning

Practice Library

Databricks exercises

Ready to ace your Databricks data engineering interview?

Prepare for your Databricks data engineering interview

Lakehouse architecture and pipeline design cases calibrated to Databricks’ Delta Lake-first, open-source data engineering culture.

Socratify AI

The Interview

What Databricks is looking for

Pipeline Design Interview

Data Pipeline Architecture

01Delta Lake Ingestion & ACID Transactions

02Exactly-Once Semantics Design

03Schema Evolution & Partition Strategy

04Lakehouse Performance Optimization

Platform Case Interview

Data Platform Diagnostics

01Spark Job Skew Root Cause Analysis

02Structured Streaming Lag Diagnosis

03Cluster Sizing & Cost Optimization

04Data Quality Framework Design

Behavioral Interview

01Data Reliability Ownership

02Cross-Team Pipeline Adoption

03Technical Complexity Communication

04Production Incident Learning

Delta Lake internals and exactly-once design//Spark skew and cluster optimization cases//Lakehouse architecture trade-off reasoning

How It Works

Master Databricks Data Engineering Interviews

Lakehouse pipeline and Delta Lake diagnostic cases scored on Databricks evaluation dimensions. · Click to expand

1Your Data Engineering Goals

Pipeline Design

Practitioner

Data Quality

Practitioner

2Data Engineering Case Prep

Design a Delta Lake ingestion pipeline for 500TB/day with exactly-once semantics, schema...

CASE INTERVIEWHard

Databricks

3Frameworks

Framework

Are ingestion-layer failures causing the delay?

Is source API throttling or connector backpressure blocking ingestion?

Fivetran connector retry backlog: 4.2M rows pending (target: <200K)

Source API rate limit hit 3× in 24 hrs: 429 errors spiked to 18%

Has data volume growth outpaced ingestion capacity?

Event volume grew 3.8× in 90 days (new product launch)

Kafka consumer lag on orders topic: 4.7M events (normal: <50K)

Are transformation bottlenecks the primary cause?

Is Spark job skew causing excessive stage runtimes?

P99 Spark stage time: 4.2 hrs (SLA: 30 min) — 1 key holds 62% of data

Task straggler rate: 34% of tasks >5× median runtime

Are dbt model dependencies creating serial transformation chains?

7 of 12 critical models run sequentially (parallelizable)

Full refresh on 3 models re-scans 1.4TB daily — no incremental strategy

Are orchestration failures causing downstream lag?

Is Airflow task queue backlogged from cascading retries?

Active DAG queue: 847 tasks (baseline: 60) — 14× normal

3 upstream DAGs set to retry 5× each with no SLA-aware delay

Is SLA miss detection too slow to trigger escalation?

SLA alert fired 4.5 hrs after breach (monitoring interval: 1 hr)

No freshness checks on 8 of 12 critical tables (Great Expectations gaps)

4Feedback

Pipeline Architecture

Distinctive

Data Reliability

Strong

Freshness & SLA Reasoning

Meeting Bar

Schema Management

Strong

Repeat

Practice Library

Databricks exercises

Design a Delta Lake ingestion pipeline handling 500TB/day from 40 sources with...

CASE INTERVIEWHard

Data / Lakehouse · Pipeline ArchitectureDatabricks

A Spark Structured Streaming job processing 2M events/min has p99 stage time of 4.2...

LEARNHard

Data / Streaming · Job Skew DiagnosisDatabricks

Your Delta Lake cluster costs grew 4× in 90 days with no new workloads. Design a cost...

LEARNMedium

Data / Cost Optimization · Cost AuditDatabricks

Tell me about a time you improved data pipeline reliability in production. What was the...

BEHAVIORALMedium

Behavioral · Reliability OwnershipDatabricks

Describe a situation where you had to migrate a legacy ETL pipeline to a modern...

BEHAVIORALMedium

Behavioral · Architecture MigrationDatabricks