Question 1
Difficulty: medium
Can you walk me through how you would design a data warehouse for a company that pulls data from a CRM, ERP, and product application?
Sample answer
I would start by clarifying the main business questions the warehouse needs to answer, because that determines the grain, history, and performance needs. Then I would inventory each source system and map out the business entities they share, like customer, order, product, and account. From there, I’d typically design a dimensional model with clear conformed dimensions and fact tables, because it makes analytics easier for both technical and non-technical users. I’d also think early about data freshness, slowly changing dimensions, and how to handle source system differences such as conflicting IDs or inconsistent timestamps. On the technical side, I’d set up layered ingestion: raw, curated, and reporting-ready. That helps with traceability and debugging. I’d also put data quality checks at each stage, plus documentation and lineage so the business trusts the numbers. My goal would be a warehouse that is scalable, understandable, and flexible enough to support future use cases without constant redesign.
Question 2
Difficulty: medium
How do you decide between a star schema and a snowflake schema in a data warehouse project?
Sample answer
I usually choose based on how the data will be used, not just on modeling preference. If the primary goal is fast analytics, simplicity, and easier self-service reporting, I lean toward a star schema. It keeps dimensions denormalized, which makes queries easier to write and usually improves performance because users don’t need to join across many tables. A snowflake schema can make sense when there are large, highly structured dimensions that benefit from normalization, or when maintaining certain hierarchies centrally is important. That said, I try not to over-normalize by default, because it often makes the warehouse harder for analysts to use. In practice, I ask whether the added complexity solves a real problem, like storage pressure, update frequency, or governance. I also consider the tooling and team skill set. If analysts are doing ad hoc work, simplicity usually wins. If the warehouse is part of a very tightly governed enterprise model, a more normalized design may be justified.
Question 3
Difficulty: hard
Describe how you would handle slowly changing dimensions in a warehouse, especially for customer data.
Sample answer
I’d first determine which attributes need historical tracking and which should simply reflect the latest value. For customer data, that distinction matters a lot because some changes, like name corrections, may not need history, while others, like segment, region, or account ownership, often do. For those historical attributes, I’d use a slowly changing dimension strategy such as Type 2, where each change creates a new row with effective dates and a current flag. That allows reporting to reflect what was true at the time, which is crucial for trends and audits. If the business only cares about the current state for certain fields, I might use Type 1 for those to avoid unnecessary row growth. I’d also make sure the ETL process compares source and warehouse values consistently, because inconsistent change detection is a common source of errors. Finally, I’d document the rules clearly so analysts know which fields are historical and which are overwritten, since ambiguity there can lead to misleading reports.
Question 4
Difficulty: medium
Tell me about a time you had to troubleshoot a data quality issue in a warehouse environment. How did you approach it?
Sample answer
I usually approach data quality issues by narrowing down where the problem was introduced rather than jumping straight to a fix. In one case, a finance dashboard showed a sudden drop in revenue that didn’t match the source system. I started by comparing the warehouse totals with the raw source extract, then checked the transformation logic and load logs. That helped me isolate the issue to a late-arriving file that had been excluded because of a timestamp-based partition rule. Once I identified the root cause, I corrected the load process to use a more reliable ingestion watermark and added a reconciliation check so the pipeline would flag missing source records before publishing downstream tables. I also communicated the impact clearly to stakeholders, including which reports were affected and when the numbers were corrected. What I learned was that data quality isn’t just about cleaning bad values; it’s about designing pipelines that make issues visible quickly and prevent silent failures. I’ve found that traceability and validation are just as important as the transformation logic itself.
Question 5
Difficulty: hard
How do you optimize a slow-running warehouse query or dashboard?
Sample answer
I start by understanding what the query is trying to do and whether the issue is with the SQL, the model design, or the underlying platform. I’ll look at the execution plan first to see where time is being spent—usually it’s in large scans, expensive joins, poor partition pruning, or unnecessary aggregations. If the query is touching too much data, I look for ways to filter earlier, reduce the number of columns selected, or pre-aggregate where it makes sense. On the modeling side, I may revisit the grain of the fact table or the size and shape of dimensions. Sometimes the real issue is that the dashboard is asking the warehouse to do work it should have already been prepared for, so I’ll introduce summary tables or materialized views if the use case is stable. I also check whether the statistics, clustering, or indexes are aligned with access patterns. I try to balance performance gains with maintainability, because the fastest solution is not always the best if it becomes brittle or difficult to support.
Question 6
Difficulty: easy
How would you explain a complex warehouse data model to a business analyst who is not technical?
Sample answer
I’d avoid starting with table names or technical jargon. Instead, I’d explain the model in terms of the business questions it supports. For example, I’d say, “This table helps us understand each sale as it happened, and this other table describes the customer and product involved in that sale.” I find that analysts usually understand dimensions and facts quickly when I relate them to real-world concepts. I also like to show one or two common reporting examples, because that makes the structure feel practical instead of abstract. If there are historical rules, like customers changing segments over time, I explain why that matters for reporting accuracy. I’ll also be transparent about tradeoffs, such as why we chose to split some data into multiple tables to improve reliability or performance. My goal is always to make the model usable, not just technically correct. When people understand how the warehouse is organized and why, they are much more likely to use it correctly and trust the numbers.
Question 7
Difficulty: medium
What steps do you take to ensure ETL or ELT pipelines are reliable and maintainable?
Sample answer
I focus on making the pipeline observable, repeatable, and easy to change safely. Reliability starts with good source handling, so I like idempotent loads, clear checkpointing, and a well-defined approach for retries and backfills. I also build validation into the pipeline rather than treating it as an afterthought. That means record counts, null checks, referential checks, and reconciliation against source totals where appropriate. For maintainability, I try to keep transformations modular and consistent in naming and structure so the next engineer can understand them without reverse engineering the whole workflow. I also separate business logic from environment-specific configuration, which makes deployments cleaner. Logging is another key piece; if a job fails, I want enough detail to know whether the issue came from source data, transformation logic, or infrastructure. I’ve found that documentation and version control are essential too, especially in teams where multiple people own the same pipelines. A reliable warehouse isn’t just one that works today; it’s one that can be operated and extended without constant firefighting.
Question 8
Difficulty: hard
Have you ever had to work with messy source systems or inconsistent business definitions? How did you handle it?
Sample answer
Yes, and that is pretty common in warehouse work. In one project, different systems defined “active customer” in slightly different ways, which caused reporting mismatches and confusion during executive reviews. My first step was to bring the relevant stakeholders together and document each definition, including where it came from and what business process it supported. I didn’t try to force a single definition immediately, because often the differences are legitimate. Instead, I helped create a canonical warehouse definition for reporting, while preserving source-specific fields where needed for traceability. On the technical side, I built transformation logic that clearly separated raw values from standardized business rules. I also added data catalog notes and report labels so users knew which metric they were looking at. That experience reinforced for me that warehouse engineering is partly technical and partly diplomacy. You can’t solve a definition problem with SQL alone. You need clear governance, documented rules, and enough transparency for people to trust the final numbers even when the source systems disagree.
Question 9
Difficulty: medium
Describe a situation where you had to balance speed of delivery with data accuracy in a warehouse project.
Sample answer
In one project, the business needed a new reporting layer quickly for a leadership review, but the source integration was still incomplete and several dimensions were not fully standardized. I knew we had to deliver something useful without creating a long-term mess. I worked with the stakeholders to define the minimum viable dataset: a limited set of key metrics, a clear refresh schedule, and explicit caveats about what was included. Technically, I built the pipeline to use a controlled subset of sources and made sure the transformations were transparent and easy to update later. I also added validation checks so we could confidently say the numbers were internally consistent, even if the model wasn’t fully mature yet. After the immediate need was met, I followed up by hardening the design, improving historical logic, and expanding the source coverage. That experience taught me that speed and accuracy don’t have to be opposites if you scope carefully, communicate honestly, and design the first version so it can evolve without being rebuilt from scratch.
Question 10
Difficulty: hard
What would you do if a stakeholder insisted that a warehouse metric was wrong, but your checks showed the data was technically correct?
Sample answer
I’d treat that as both a data issue and a communication issue. First, I’d confirm the metric definition end to end, including the SQL logic, source fields, filters, and any business rules applied during transformation. If the warehouse is technically correct, the next question is whether it is aligned with the stakeholder’s expectation. Very often, people are comparing different definitions without realizing it, or they are using a dashboard in a way it wasn’t intended. I would walk through a concrete example with the stakeholder, using a few rows or transactions so the difference is visible rather than theoretical. If the business definition really should be changed, I’d document the new rule and assess the downstream impact before making the update. If not, I’d work on clearer labeling, documentation, or training so the same confusion doesn’t keep happening. I’ve learned that “correct” is not always enough in analytics. The warehouse has to be both accurate and understandable, otherwise the business won’t trust or use it effectively.
