Data Engineer
Practice Data Engineer interview questions on SQL, ETL and ELT, Spark, data modeling, streaming, orchestration and warehouse design.
- Questions
- 100
- Interview Qs
- 30
- Judged problems
- 92
- Skills
- 9
About the role
A Data Engineer builds and runs the pipelines that move data from apps, databases and third-party tools into a warehouse or lakehouse where analysts, data scientists and AI systems can use it. A typical day includes writing SQL and Python transformations, fixing a pipeline that failed overnight, adding a new data source, and making sure tables are fresh, correct and well documented.
The work is a mix of software engineering and data thinking. You design schemas and table layouts, choose between batch and streaming, handle late or duplicate data, and keep costs under control on platforms like Snowflake, BigQuery or Databricks. Reliability matters a lot: pipelines must be idempotent, tested and monitored so a bad load does not silently break every dashboard.
As you become more senior, you design the overall data platform, set standards for data quality and data contracts, and work with product teams so that the data they produce is usable from the start.
Practise by level
Open the full question bank (100 questions) →Mock interview
Screening round
4 questions · 4 min each · pass 50%
mediumTechnical round
5 questions · 4 min each · pass 60%
hardFinal round
6 questions · 3 min each · pass 70%
Coding problems for data engineers
- Best K-Day Sales Windoweasy
- Binary Classifier Metricseasy
- Collapse Repeated Readingseasy
- Cumulative Totalseasy
- Dedupe Records by Keyeasy
- Fixed Deposit Maturity Amounteasy
- GST Invoice Totalseasy
- Latency Percentileseasy
- Load Parcels Onto Vanseasy
- Merge Two Sorted Listseasy
- Min-Max Feature Scalingeasy
- Pairs With an Exact Price Gapeasy
SQL problems
- Customer Email Domainseasy
- Duplicate Signup Emailseasy
- Daily Active Users with a 3-Day Averagemedium
- Deduplicate Marketing Leadsmedium
- Latest Status per Shipmentmedium
- Ledger Running Balancemedium
- Quarterly Sales Pivotmedium
- Second-Highest Salary per Departmentmedium
- Top Two Products per Categorymedium
- Bill of Materials Explosionhard
Skills in this track
Learning roadmap
- 1
SQL and Python foundations
Master joins, window functions, CTEs and query plans. In Python, learn file handling, APIs, pandas and writing clean, testable functions.
- 2
Data modeling and warehousing
Learn normalisation, star schemas, fact and dimension tables, slowly changing dimensions, and how columnar warehouses like BigQuery and Snowflake work.
- 3
Pipelines and orchestration
Build batch pipelines with dbt for transformations and Airflow or Dagster for scheduling. Learn idempotency, retries, backfills and incremental loads.
- 4
Distributed processing
Learn Apache Spark: DataFrames, partitions, shuffles, joins and skew. Understand file formats like Parquet and table formats like Delta Lake and Apache Iceberg.
- 5
Streaming and change data capture
Learn Kafka topics, partitions and consumer groups, event time vs processing time, watermarks, and CDC tools such as Debezium.
- 6
Quality, cost and operations
Add data tests, freshness checks and lineage. Learn to monitor pipelines, control cloud cost and write data contracts with upstream teams.
Top Data Engineer interview questions
- Describe a data pipeline you built or would build, from source to dashboard.
- What is the difference between an OLTP database and an OLAP data warehouse?
- Explain a star schema and why it is used in data warehouses.
- What data quality checks would you add to a pipeline?
- Why use an orchestration tool like Airflow instead of cron jobs?
- What is a slowly changing dimension, and how do SCD Types 1, 2 and 3 differ?