Question 1
Difficulty: medium
Can you walk me through how you would build a simple ETL pipeline for a daily sales report?
Sample answer
I would start by clarifying the business goal, the source systems, and the required refresh time for the report. For a daily sales pipeline, I’d typically extract data from a transactional database or API into a raw landing area first so I always keep an untouched copy of the source. Then I’d validate basic quality checks such as missing order IDs, duplicate records, and date consistency before transforming the data. In the transform step, I’d standardize fields, join reference tables like product or store dimensions, and calculate metrics such as daily revenue, units sold, and average order value. After that, I’d load the cleaned data into a warehouse table that supports reporting. I’d also make the pipeline idempotent so reruns don’t create duplicates, and I’d add logging and alerting so failures are easy to spot. For a junior role, I think being organized, reliable, and careful with data integrity matters as much as writing the code.
Question 2
Difficulty: medium
How do you handle data quality issues when you discover bad records in a pipeline?
Sample answer
My first step is to understand the scope of the issue and whether it affects the current run, historical data, or both. I don’t want to guess, because a small data issue can become a reporting problem if it is not isolated properly. I would check whether the bad records come from a source system problem, a transformation bug, or an upstream schema change. Then I’d decide whether to quarantine the records, apply a safe business rule, or stop the pipeline depending on how severe the issue is. I like using validation checks such as null thresholds, duplicate detection, and range checks so the problem is caught early. If the issue is recurring, I’d document it and help create a more permanent fix, such as adding better input validation or improving source contracts. I also think communication matters: stakeholders should know when data is incomplete and what impact it has on reporting, rather than finding out later from a dashboard mismatch.
Question 3
Difficulty: easy
What is the difference between ETL and ELT, and when would you use each approach?
Sample answer
ETL and ELT both move data from sources into a warehouse or analytics platform, but the order of transformation is different. With ETL, I extract the data, transform it before loading, and then store the cleaned result. That approach is useful when the destination is limited or when the data needs to be heavily cleaned before it can be loaded. With ELT, I extract and load first, then transform inside the warehouse using its processing power. That works well with modern cloud data platforms because they are built to handle large volumes and SQL-based transformations efficiently. In practice, I’d choose ETL if I need strict control over data before it lands in the destination or if the source data is sensitive and should be minimized early. I’d choose ELT when the warehouse is strong enough to handle transformations and when I want faster ingestion with more flexibility. As a junior engineer, I’d focus on matching the method to the toolset and the business need, not just following one pattern by default.
Question 4
Difficulty: hard
How would you troubleshoot a pipeline that suddenly started running much slower than usual?
Sample answer
I’d approach it systematically so I can identify whether the slowdown is caused by source, compute, query, or network issues. First, I’d compare the current run with previous successful runs to see which stage increased in duration. If extraction is slower, I’d check source system performance, query plans, and whether the input volume changed. If the transform step is the bottleneck, I’d look at joins, filters, partitions, and whether the job is processing more data than expected. If the load step is slow, I’d check warehouse capacity, locking, file sizes, and batch frequency. I’d also look for recent code changes, schema changes, or a spike in duplicate records that could increase processing time. I like to use logs and metrics rather than assumptions because they shorten the time to root cause. Once I identify the issue, I’d fix the immediate problem and think about prevention, such as indexing, partitioning, incremental loads, or better monitoring. For me, stable performance is part of data reliability.
Question 5
Difficulty: easy
Tell me about a time you had to learn a new tool or technology quickly for a project.
Sample answer
In a project setting, I had to pick up a new data workflow tool quickly because the team wanted a more maintainable way to manage scheduled jobs. I did not try to learn everything at once. Instead, I focused on the parts that would help me deliver: how to define jobs, configure dependencies, monitor runs, and handle failures. I built a small test workflow first so I could understand the behavior in a low-risk environment. That gave me confidence before I touched the production-like pipeline. I also read the documentation with a practical goal in mind, which helped me avoid spending time on features we were not going to use. When I got stuck, I asked targeted questions and shared what I had already tried, which made the conversations more efficient. By the end, I was able to contribute to the implementation and help document the setup for the rest of the team. That experience taught me that I learn best by combining hands-on practice with focused research.
Question 6
Difficulty: medium
How do you write SQL that is readable and maintainable for other engineers?
Sample answer
I try to write SQL that someone else can understand without needing extra explanation. That means using clear table aliases, consistent indentation, and meaningful names for intermediate CTEs and derived fields. I avoid overly nested queries when a CTE would make the logic easier to follow. I also prefer breaking complex transformations into smaller steps so each part has a single purpose, such as filtering, joining, or aggregating. Another habit I follow is being explicit about join conditions and filtering logic, because hidden assumptions often create bugs later. If a query is part of a repeated pipeline, I also think about performance and readability together, not as separate goals. I’ll comment only when the logic is not obvious, not to explain every line. Before I consider a query finished, I test it with edge cases and check that the output matches expectations. In a team setting, clean SQL saves time during reviews, reduces mistakes, and makes it easier for someone to maintain the pipeline months later.
Question 7
Difficulty: medium
Describe a time when you had to work with incomplete or messy data. What did you do?
Sample answer
I once worked with a dataset where several key fields were missing or formatted inconsistently, which made it hard to use for reporting. My first step was to understand which fields were essential for the business case and which were optional. That helped me avoid overcorrecting the data. I then profiled the dataset to see patterns in the missing values and the inconsistent formats, such as dates in different styles and categorical values with spelling variations. For the transformation, I standardized the obvious issues and created rules for handling blanks or ambiguous values based on business logic. When I could not confidently infer the correct value, I kept the record but marked it as incomplete so downstream users knew it needed caution. I also documented the data issues and shared them with the relevant team so they understood the impact at the source. What I learned is that messy data is not unusual; what matters is being transparent, careful, and consistent in how you handle it.
Question 8
Difficulty: hard
How would you design a pipeline to be idempotent?
Sample answer
To make a pipeline idempotent, I’d design it so running it multiple times produces the same final result instead of duplicating or corrupting data. One common approach is to load data into staging tables first, then replace or merge records in the target table using a stable key and a clear update rule. For daily batch jobs, I’d often partition the data by date and reprocess only the affected partition, which makes reruns safer and more efficient. I’d also avoid blindly appending records unless I have strong guarantees that the input is strictly new and unique. If I’m dealing with file-based ingestion, I’d track processed file names or batch IDs so the same file is not loaded twice. In addition, I’d make sure the pipeline can recover from failure mid-run by using checkpoints or transactional steps where possible. I think idempotency is important because production jobs fail eventually, and the real test of a pipeline is whether it can be rerun safely without manual cleanup.
Question 9
Difficulty: medium
What would you do if a stakeholder asked for a report before the pipeline was fully ready?
Sample answer
I would be honest about what is ready, what is not, and what the risk is if we rush it. I’d first ask what decision the report supports, because that helps me understand whether a partial solution could still be useful. If the business need is urgent, I’d look for a safe workaround, such as providing a limited version of the report with a clear timestamp, known gaps, or narrower scope. I would not want to deliver something that looks complete but is actually unreliable. At the same time, I’d communicate the trade-offs in plain language so the stakeholder can make an informed decision. If the request is important enough, I’d work with the team to prioritize the most critical data elements first and defer the rest. I think a junior data engineer should be dependable and transparent, especially when the pressure is high. The goal is not just to deliver quickly, but to protect trust in the data and avoid creating confusion later.
Question 10
Difficulty: easy
Why do you want to work as a Junior Data Engineer, and what strengths would you bring to the team?
Sample answer
I want to work as a Junior Data Engineer because I like building the systems that make data useful and trustworthy. I find it satisfying to turn raw, messy inputs into something people can actually rely on for analysis and decision-making. I also like the mix of logic, problem-solving, and collaboration in the role. My strengths are that I’m careful, consistent, and comfortable digging into details without losing sight of the bigger picture. I tend to ask good questions early, which helps me avoid making assumptions in data work. I also communicate well when something is unclear, because I know that in data engineering, small misunderstandings can turn into larger pipeline issues. I’m motivated to keep learning, especially around SQL, Python, orchestration, and data modeling. At the same time, I try to be practical and focused on delivering value, not just experimenting with tools. I’d bring a strong work ethic, a willingness to learn, and a habit of treating data quality as a serious responsibility.
