How do you decide between using ETL and ELT for a data pipeline?

Start by explaining the core difference: ETL extracts, transforms, then loads, while ELT loads raw data first and performs transformations inside the warehouse. Frame your decision around data volume, transformation complexity, and the processing power of your destination system. Give a concrete example: if you're ingesting large event streams into a cloud columnar warehouse like Snowflake, ELT often works because the warehouse can scale compute for transformations. Conversely, if you must clean PII or reduce data volume before hitting a cost-sensitive lake, an ETL job that filters and masks data beforehand may be better. Tip: document cost implications and failure modes for each approach, and run a short proof of concept to measure transform time and costs. Avoid deciding on architecture based on vendor marketing, and always plan for reprocessing and schema evolution.

Explain star schema and snowflake schema and when you'd use each.

Describe both schemas and the primary trade-offs: a star schema has one denormalized fact table with dimension tables optimized for query simplicity, while a snowflake schema normalizes dimensions to reduce redundancy. Tie the choice to query patterns, maintenance effort, and storage considerations. Provide an example: use a star schema for a sales analytics warehouse where analysts run frequent aggregates across time and product, because fewer joins speed up queries. Use a snowflake schema when dimension cardinality is high and normalization reduces storage and update complexity, for example complex product hierarchies across suppliers. Advice: prototype typical queries and measure execution time and cost before finalizing the model. Avoid over-normalizing small dimensions and keep surrogate keys consistent to reduce downstream errors.

How do you implement slowly changing dimensions (SCDs)?

Start by naming SCD types you know, usually Type 1, Type 2, and Type 3, and explain when each is appropriate. Describe the business need: Type 1 overwrites historical values, Type 2 preserves history with effective date ranges or current flags, and Type 3 stores limited history in additional columns. Give a specific technique: implement Type 2 with a surrogate key, valid_from and valid_to timestamps, and a current_flag. For example, when a customer's address changes you insert a new row with a new surrogate key and set valid_to on the previous row, which preserves historical joins for past transactions. Tip: automate SCD flows with idempotent jobs and clear reconciliation reports to catch missed changes. Avoid relying solely on last_updated timestamps without reconciliation, because updates can arrive out of order or be backfilled.

What strategies do you use to optimize query performance in a data warehouse?

Begin with a multi-pronged approach: review physical schema, partitioning and clustering, statistics and query plans, and the SQL itself for anti-patterns like cross joins. Prioritize optimizations based on frequency and cost of queries, not just theoretical improvements. Give practical steps: add clustering keys or partition by date for large fact tables, push selective filters earlier, and use materialized views for expensive aggregates. For example, precompute daily aggregates for a dashboard that queries multi-month data, and test query plans to confirm the materialized view is used. Watch out for common pitfalls such as over-clustering small tables or creating many low-value materialized views that increase maintenance cost. Monitor query runtime and cost metrics after changes to ensure improvements hold under real workloads.

How do you design an incremental load for large tables?

Explain the approach: detect new or changed rows, extract only delta data, and apply idempotent upserts or inserts in the target. Define the change detection mechanism, which can be change data capture (CDC), timestamps, or high-water mark tracking, and explain trade-offs for each. Provide an example: implement CDC using database logs for near-real-time changes, and write an idempotent upsert that merges records into the dimension table based on a business key plus a last_modified timestamp. For batch windows, use a high-water mark column and verify with a checksum or row counts to detect missed ranges. Tip: include reconciliation and backfill processes so you can handle late-arriving data. Avoid relying on non-monotonic timestamps alone when upstream systems can rewrite history; CDC is safer where available.

How do you monitor and alert on data pipeline failures and data quality issues?

Outline a monitoring strategy that covers job health, SLA adherence, and data quality metrics like row counts, null rates, and distribution checks. Explain using a mix of infrastructure alerts for failures and data-level checks for quality issues, with escalation paths and runbooks. Give a concrete setup: emit metrics from your ETL jobs to a monitoring system, create alerts for job failures and SLA misses, and run nightly assertions that check row counts, key uniqueness, and column-level ranges. For example, a sudden drop in rows for a source table should trigger an incident with a link to recent pipeline logs. Practical tip: implement self-healing where safe, such as automatic retries with exponential backoff, but always surface persistent failures to an on-call engineer. Avoid silent failures by ensuring all checks produce actionable alerts and include links to query samples to speed debugging.

Describe how columnar storage differs from row storage and when each is preferable.

Start by explaining that columnar storage stores data column by column which reduces I/O for analytical queries that touch a subset of columns, while row storage stores entire rows which is efficient for transactional workloads and single-row operations. Connect the choice to query patterns and compression benefits. Provide an example: use a columnar warehouse like BigQuery or Snowflake for ad hoc analytics and large aggregations because compression and vectorized execution speed aggregations over millions of rows. Use row stores like Postgres for OLTP systems, small lookup tables, or when you need frequent single-row updates. Tip: when mixing workloads, separate systems or use hybrid architectures to avoid contention and cost surprises. Avoid forcing transactional update patterns into a columnar store without careful design, since update costs can be high.

How do you handle schema evolution in production pipelines?

Start by describing a versioning approach: use explicit schema migration scripts, maintain compatibility rules, and version your schema and pipeline code together. Define backward and forward compatibility policies so you can deploy changes without breaking downstream consumers. Give an example workflow: use a CI process that validates new schema changes against a sample dataset, deploy additive changes first (like new nullable columns), and schedule breaking changes with a coordinated consumer migration window. For instance, add a new column with default nulls, update consumers, then remove legacy fields once all consumers are validated. Advice: include automated contract tests and a staged rollout to catch issues early, and keep a clear rollback plan for schema changes. Avoid ad-hoc schema edits in production without tests and coordination, because they often cause surprise downstream failures.

What is data partitioning and how do you choose partition keys?

Explain partitioning as dividing large tables into smaller segments stored and scanned independently, which reduces query I/O for selective filters. Discuss common partitioning strategies like range partitioning on date, hash partitioning on an ID, or list partitioning for categorical fields, and relate choices to query patterns. Give a real example: partition a clickstream fact table by event_date when most queries filter by date, which reduces scanned data by pruning partitions. For very high-cardinality joins, combine partitioning with clustering or sorting on frequently filtered columns to improve pruning and locality. Tip: measure partition pruning using explain plans and avoid too many small partitions, which increases metadata overhead. Avoid partitioning by low-selectivity columns that will not reduce scan size significantly.

How do you approach data modeling for a new analytics domain?

Begin with stakeholder interviews to understand the core business metrics and common queries, and sketch a logical model mapping source systems to facts and dimensions. Focus on creating a clear fact table for events or transactions and dimension tables that capture stable descriptive attributes, and iterate with consumers early. Provide a technique: build a canonical event schema, capture raw events in a staging area, then produce curated star schemas for reporting. For example, if you model order analytics, identify order fact granularity, link dimensions such as customer, product, and time, and validate sample queries with analysts to confirm the model meets needs. Tip: keep an eye on cardinality and join paths to avoid exploding join complexity, and document the grain and assumptions for each table. Avoid finalizing the model without real query profiling, because actual use can reveal unexpected requirements.

Tell me about a time you handled a major production data outage.

Situation: Our nightly sales ETL failed three nights in a row, leaving dashboards with stale data and stakeholders escalating requests. Task: I was asked to restore fresh data, find the root cause, and prevent recurrence while minimizing business impact. Action: I triaged by checking job logs and discovered a downstream schema change caused a merge step to fail, so I wrote a hotfix that adjusted the merge logic and reprocessed the affected partitions. I then implemented a pre-deploy contract test for the schema and added an alert for schema drift to catch similar problems earlier. Result: We restored data within four hours, reduced stakeholder escalations, and the new tests prevented recurrence for subsequent schema changes. The team regained trust, and dashboard freshness improved to meet SLAs.

Describe a project where you improved ETL performance under a tight deadline.

Situation: We needed to cut nightly pipeline runtime from six hours to under two for a reporting deadline next month. Task: My goal was to reduce runtime without risking data correctness and while coordinating with a small team under time pressure. Action: I profiled the slowest steps, identified a costly full-table scan, and introduced partition pruning and incremental processing for that step, while adding parallelism in safe places and scheduling resource-hungry jobs off-peak. I also created reproducible benchmarks so we could measure improvements after each change. Result: We reduced the runtime to 90 minutes and met the reporting deadline, and the incremental approach lowered compute cost by roughly 40 percent for that pipeline. Team processes improved because we added profiling and benchmarks to our standard deployment checklist.

Give an example of a time you disagreed with a stakeholder about requirements and how you resolved it.

Situation: A product manager wanted daily denormalized customer snapshots that duplicated source logic, which risked inconsistent metrics and high storage cost. Task: I needed to align on a solution that provided the product’s insights without causing drift from canonical metrics. Action: I scheduled a meeting with the product manager and a data analyst, demonstrated how differing definitions would produce inconsistent reports, and proposed a single source of truth approach with a documented canonical customer view and an added reporting layer for product-specific joins. I offered a short migration plan and a reconciliation report to verify equivalence during rollout. Result: The stakeholder agreed to the canonical view, we implemented the migration with reconciliation checks, and product dashboards showed consistent metrics across teams. This reduced duplicated work and prevented subtle reporting disagreements going forward.

data warehouse engineer Interview Questions: Complete Guide

Data warehouse engineer interview questions typically cover data modeling, ETL/ELT design, performance tuning, and production reliability. Expect a mix of whiteboard design, SQL exercises, and system design or behavioral questions, and you should be ready to explain trade-offs and past results. Stay calm, explain your assumptions, and show how you think through risks and testing.

Common Interview Questions

Behavioral Questions (STAR Method)

STAR Method: Structure your answers using Situation, Task, Action, and Result to tell compelling stories about your experience.

Questions to Ask the Interviewer

Show your interest by asking thoughtful questions

•What does success look like in this role after the first 6 months and what are the highest-priority projects?
•Can you describe the team structure and how data engineering, analytics, and platform teams collaborate here?
•What are the largest pain points you face with your current data pipelines or warehouse cost management?
•How do you measure data quality and ownership across teams, and who is responsible for incident triage?
•What constraints or compliance requirements, such as data residency or PII handling, should the incoming engineer expect to manage?

Interview Preparation Tips

Practice explaining past projects with clear metrics and trade-offs, focusing on what you changed and why, not only what you built.

Prepare a short SQL exercise by practicing window functions, common table expressions, and efficient joins on sample datasets to show readable and correct solutions.

Bring questions about monitoring, SLAs, and on-call expectations so you know the operational context and can show you think about reliability.

When asked system design or modeling questions, state your assumptions, sketch the simplest working solution, then iterate on performance and reliability improvements.

Overview

This guide prepares candidates for data warehouse engineer interviews by focusing on the three question types interviewers ask most: hands-on technical problems, system-design scenarios, and behavioral questions about past projects. Expect about 60–70% of questions to probe SQL and ETL skills, 20–30% to test architecture and scaling decisions, and the rest to assess teamwork and trade-offs.

Focus areas include:

•SQL performance: write and optimize queries that handle 10M+ rows; typical goals are sub-2 second point lookups and sub-10 second aggregated queries for common dashboards.
•Data modeling: design star and snowflake schemas for 100–500 dimensional attributes; demonstrate normalization vs. denormalization trade-offs.
•Ingestion and pipelines: show hands-on experience building pipelines that sustain 0.5–2M rows per minute or guarantee end-to-end latency under 15 minutes for hourly loads.
•Cloud platforms: Redshift, Snowflake, BigQuery — discuss cost-per-TB figures, clustering keys, and storage vs compute separation.

Interviewers expect concrete examples: cite the size of datasets, performance gains (e. g.

, “reduced query scan by 92% using partition pruning”), and specific tools used (Airflow, dbt, Spark). Use numbers and before/after metrics when describing achievements.

Actionable takeaway: prepare two 90-second stories with metrics—one about a performance fix and one about a system design that scaled to support at least 50 concurrent users.

Key Subtopics to Study

Study these concentrated subtopics and prepare short examples you can explain in 60–120 seconds.

•Write window functions, CTEs, and efficient JOINs for tables with 10M–100M rows.
•Explain index usage, partition pruning, and reducing scanned data by 70–95%.

•Design a star schema for sales data (fact table: 200M rows; dimension tables: 10–50 columns).
•When to normalize vs denormalize; show storage and query-cost trade-offs with numbers.

•Show examples using Airflow or dbt: DAGs that process 1M rows/min or perform hourly batch loads.
•Handle backfill strategies and idempotency; explain checkpointing and retries.

•Compare Redshift, Snowflake, BigQuery on concurrency (e.g., 100 vs 1,000 concurrent queries) and pricing models.

•When to use Kafka/Streaming: sub-minute freshness for 50–200K events/min.

•Use metrics: query latency percentiles (p50/p95), storage costs per TB, and SLOs.

•Implement RBAC, column masking, and GDPR-compliant retention policies.

Actionable takeaway: prepare a 2-minute pitch per subtopic with one metric, one tool, and one trade-off.

Study Resources and Practice Materials

Use a mix of books, docs, courses, and hands-on projects. Allocate 4–8 weeks of focused study split between theory and practice.

Recommended books

•The Data Warehouse Toolkit (Ralph Kimball) — read chapters on dimensional modeling; apply to a 100M-row sales dataset.
•Designing Data-Intensive Applications (Martin Kleppmann) — focus on storage engines and stream processing chapters.

Online courses and tutorials

•Coursera: Data Warehousing for Business Intelligence — 4 weeks, 3–5 hours/week.
•Udacity/EdX: BigQuery and Redshift workshops — follow cloud labs to load 100GB and run 1,000 queries.

Documentation and best-practices

•AWS Redshift best practices: read sections on distribution keys and VACUUM/ANALYZE.
•Snowflake docs: clustering keys and micro-partition pruning.
•Google BigQuery performance docs: partitioning and slot management.

Hands-on practice

•LeetCode SQL and Mode Analytics SQL tutorials: practice 100+ queries across joins, window functions, and aggregation.
•GitHub sample projects: TPC-DS dataset loaders, dbt starter kits, and Airflow DAG examples.
•Sample tasks: build an ETL that ingests 500K rows/hour, transforms into a star schema, and reduces dashboard query time by 60%.

Actionable takeaway: pick one book, one cloud doc, and one hands-on project; complete them in the first 30 days and measure improvements with concrete metrics.

data warehouse engineer Interview Questions: Complete Guide

Michael Rodriguez

Common Interview Questions

Behavioral Questions (STAR Method)

Questions to Ask the Interviewer

Interview Preparation Tips

Overview

Key Subtopics to Study

Study Resources and Practice Materials

Interview Prep Checklist

Build your job search toolkit

data warehouse engineer Interview Questions: Complete Guide

Michael Rodriguez

Common Interview Questions

Q1How do you decide between using ETL and ELT for a data pipeline?

Q2Explain star schema and snowflake schema and when you'd use each.

Q3How do you implement slowly changing dimensions (SCDs)?

Q4What strategies do you use to optimize query performance in a data warehouse?

Q5How do you design an incremental load for large tables?

Q6How do you monitor and alert on data pipeline failures and data quality issues?

Q7Describe how columnar storage differs from row storage and when each is preferable.

Q8How do you handle schema evolution in production pipelines?

Q9What is data partitioning and how do you choose partition keys?

Q10How do you approach data modeling for a new analytics domain?

Behavioral Questions (STAR Method)

B1Tell me about a time you handled a major production data outage.

B2Describe a project where you improved ETL performance under a tight deadline.

B3Give an example of a time you disagreed with a stakeholder about requirements and how you resolved it.

Questions to Ask the Interviewer

Interview Preparation Tips

Overview

Key Subtopics to Study

Study Resources and Practice Materials

Interview Prep Checklist

Build your job search toolkit