Question 1
Difficulty: medium
Walk me through how you would plan a data migration from an old system to a new platform.
Sample answer
I’d start by understanding the business goals, because migration isn’t just about moving records—it’s about protecting operations. First I’d inventory the source data, identify owners, and classify what needs to move, what needs to be transformed, and what can be retired. Then I’d assess data quality, dependencies, volumes, and any compliance constraints. From there, I’d define mapping rules, validation criteria, and a testing strategy with clear sign-off points. I’d also build a cutover plan that includes timing, rollback options, and communication steps for stakeholders. In parallel, I’d work with technical teams to confirm interfaces, downstream systems, and access requirements. My approach is always to reduce surprises: prototype early, reconcile counts often, and keep business users involved so the migrated data works the way they expect on day one. A successful migration is the one that feels boring to users because everything just works.
Question 2
Difficulty: medium
How do you handle messy or inconsistent source data before a migration?
Sample answer
I treat messy data as a normal part of the job, not an exception. My first step is to profile the data so I can see the real issues: duplicates, missing fields, inconsistent formats, invalid values, and records that don’t match business rules. Then I separate problems into what can be fixed automatically, what needs business clarification, and what should be excluded. I like to create data quality rules early and get agreement from stakeholders, because “cleaning” data can quickly turn into opinion-based decisions unless the rules are documented. If needed, I’ll run cleansing scripts or build transformation logic to standardize values, normalize dates, and de-duplicate records. I also keep an audit trail so we know what was changed and why. The goal is not perfection; it’s trustworthy data that supports the new system and doesn’t create hidden issues after go-live.
Question 3
Difficulty: medium
Describe a time you had to troubleshoot a migration issue close to go-live.
Sample answer
In one migration, our test reconciliations were fine until we reached final user acceptance, where a set of customer records was showing incorrect account status values. I immediately checked the transformation logic and found the source system stored status codes differently across regions, and one code mapping had been missed in the original design. Rather than patching it blindly, I worked with the business owner to confirm how those statuses should be interpreted in the new system, then updated the mapping table and reran the impacted records through a controlled validation cycle. We also added a check to flag any unmapped codes before final load. What helped most was staying calm, communicating clearly, and focusing on the root cause instead of the symptom. We still launched on time, and the fix improved our migration controls for later phases as well.
Question 4
Difficulty: medium
What validation steps do you use to make sure migrated data is accurate?
Sample answer
I use validation at multiple levels because a single check is never enough. At the most basic level, I compare record counts between source and target, but I don’t stop there. I verify key fields, totals, and control sums for critical datasets, especially where financial or operational impact is high. I also run business-rule checks, such as required-field completeness, referential integrity, and allowed-value validation. For complex migrations, I’ll sample records end to end to confirm the transformed data behaves correctly in the target application, not just in a flat file or staging table. If possible, I involve business users in reviewing a subset of records that represent real scenarios. I document all discrepancies, classify them by severity, and track resolution before sign-off. Accuracy isn’t just about matching numbers; it’s about ensuring the migrated data supports real business processes without hidden defects.
Question 5
Difficulty: easy
How do you prioritize migration work when there are competing deadlines from IT and business teams?
Sample answer
I prioritize based on business impact, dependency timing, and risk. If IT wants infrastructure tasks and the business wants validation completed, I first map what is blocking what. That gives me a clear picture of the critical path. I’m careful not to treat every request as equal, because that usually leads to frantic work and missed details. I’ll identify the tasks that directly affect go-live readiness, data quality, or downstream integration and focus there first. Then I communicate trade-offs in plain language so stakeholders understand why one item can’t wait while another can. I’ve found that a short, visible plan with owners and dates reduces tension quickly. If needed, I’ll suggest phased delivery so we can move the highest-risk or highest-value data first. My goal is always to keep momentum without sacrificing control, because a migration only works when both the technical and business sides stay aligned.
Question 6
Difficulty: easy
How would you approach mapping fields between two systems that use different data models?
Sample answer
I’d start by understanding the business meaning behind each field, not just the technical name. Two systems can label something the same way but store it differently, so I first compare definitions, data types, allowed values, and usage in business processes. Then I build a mapping document that shows source-to-target relationships, transformation rules, default values, and any exceptions. For example, one field might split into multiple target fields, or several source statuses might collapse into one target category. I like to validate the mapping with subject matter experts early, because they often catch assumptions that technical teams miss. For complex logic, I’ll create examples using real records so everyone can see how the mapping works in practice. I also make sure the mapping is version-controlled, because changes happen during migration projects. Good mapping is really about preserving meaning, not just moving columns from one table to another.
Question 7
Difficulty: medium
Tell me about a time you had to work with stakeholders who disagreed on data definitions.
Sample answer
I once worked on a migration where Finance and Operations had different definitions for the same customer status fields. Finance wanted statuses based on billing activity, while Operations wanted them based on service usage. Rather than forcing a quick decision, I set up a working session and walked both groups through sample records and how the status would affect the new system’s reporting and workflows. That made the disagreement much more concrete. I documented both interpretations, highlighted where each was used, and helped the business sponsor decide which definition should be authoritative for the target system. In one case, we kept one field as the primary status and added a secondary attribute so neither team lost the information they needed. What I learned from that situation is that migration projects often surface hidden business ambiguity. My job is to turn that ambiguity into clear rules that everyone can support before data is loaded.
Question 8
Difficulty: easy
What experience do you have with ETL tools or scripting for migration tasks?
Sample answer
I’ve used ETL tools and scripting together because I think the best migrations combine structure with flexibility. In ETL platforms, I’m comfortable building extraction, transformation, and load workflows, setting up lookups, data type conversions, and error handling. I also use scripts when I need more control over cleansing, validation, or exception handling, especially for large or unusual datasets. My priority is always reliability and traceability, so I build logs, reject handling, and checkpoints into the process. If a load fails halfway through, I want to know exactly where it stopped and how to restart safely. I also try to keep logic modular so changes are easier to test and maintain. For me, tools are less important than the discipline behind them: clear requirements, repeatable runs, and strong validation. A good migration process should be understandable enough that someone else can support it if needed.
Question 9
Difficulty: hard
How do you manage risk during a large-scale data migration?
Sample answer
I manage risk by identifying it early and making it visible. Before migration begins, I review the biggest sources of failure: bad data quality, incomplete mapping, missing dependencies, performance issues, unclear ownership, and weak cutover planning. Then I rank those risks by probability and impact so we know where to spend effort. For high-risk areas, I like to use proof-of-concepts, small pilot loads, and repeated test cycles rather than waiting for one big final run. I also make sure there’s a rollback plan and clear decision points if something goes wrong. Just as important, I keep communication tight—weekly status, issue logs, and escalation paths help teams respond quickly instead of reacting late. I’ve found that migration risk drops a lot when everyone knows the plan, the fallback, and their role in both. A well-managed migration is one where the team is prepared for problems without being surprised by them.
Question 10
Difficulty: hard
If a business user says the migrated data looks correct in counts but wrong in day-to-day use, how would you investigate?
Sample answer
I’d treat that as a sign that the migration passed technical validation but missed a business rule or workflow expectation. My first step would be to ask for specific examples: which records, which screens, what behavior looks wrong, and how the data is supposed to drive the process. Then I’d trace those records back through the mapping and transformation logic to see whether the issue is in extraction, conversion, or load behavior. I’d also check whether the target system applies default values, triggers, or permissions that change how data appears after import. In many cases, the issue isn’t the data itself but the way the target application interprets it. I’d document the root cause, involve the business owner in confirming the correct behavior, and update the validation checklist so the same gap doesn’t happen again. Technical accuracy matters, but functional accuracy is what users actually experience.
