The SQL Metrics Interviewers Really Care About (From Real Interviews)

After a few SQL interviews, it’s easy to assume every company tests something different. After 11 of them, that assumption stopped holding up. Despite differences in companies and formats, the same metric questions kept coming back. This article summarizes what I saw most often and what those patterns say about how SQL interviews really work.

Between November 2023 and April 2025, I took part in 11 data analyst interviews that included SQL technical screens. While the companies and formats varied, the SQL questions themselves followed clear and repeatable patterns. The same types of metrics appeared again and again, regardless of company size or industry.

In this article, I analyze SQL interview questions from those interviews and categorize them using the SQL Metric Patterns introduced in earlier articles, including the Sales Growth Dataset Exploration series. Instead of exploring metrics within a single dataset, this article focuses on how SQL interview questions cluster around specific metric types.

The goal of this article is straightforward: to show which SQL metrics appear most often in real interviews so you can prioritize your preparation more effectively. Rather than trying to cover every possible SQL topic, this breakdown highlights where study time tends to deliver the highest return. This is also why structured SQL practice focused on real reporting problems—such as the exercises used in LearnSQL.com courses—tends to align more closely with what interviews actually test than ad-hoc or puzzle-style SQL prep.

Along the way, I also touch on how SQL is tested in interviews, how answers are evaluated, and why certain metric patterns show up far more frequently than others. This is not a guide to interview strategy by company or industry, and it does not include exact questions or company names. The focus is on recurring patterns and what they reveal about the core SQL skills interviewers consistently test.

If you are preparing for SQL-heavy data analyst interviews and want to focus on what actually shows up in practice, this article summarizes the patterns I kept encountering and the lessons they imply.

SQL Testing Methods Encountered in Interviews

Across the 11 interviews, SQL was evaluated using three distinct testing formats. While the underlying SQL skills were similar, the format of the test significantly influenced how answers were judged and what interviewers appeared to prioritize.

Timed Online Portals

The first format was a timed online portal, typically hosted on platforms such as HackerRank. These tests were constrained to roughly 20–50 minutes and usually consisted of one or two SQL questions. Because the environment is standardized and unassisted, questions tend to be simpler in structure but strict in execution. Correctness and speed matter most, and solutions are generally evaluated as pass or fail.

Live SQL Interviews Using a Shared IDE

The second format was a live SQL interview conducted in a shared IDE during a virtual session. In these interviews, I was often allowed to write SQL without executing it, which reduced the emphasis on exact syntax and shifted focus toward logical correctness and reasoning. Because syntax errors were less costly, interviewers tended to ask more questions or introduce ambiguity, sometimes layering follow-ups on top of an initial prompt. Evaluation in this format was rarely binary and instead reflected how clearly the reasoning was communicated.

Take-Home SQL Assignments

The third format was a take-home SQL assignment, typically delivered as a document containing schemas, table creation statements, and multiple questions. These assignments were usually given one to two days for completion and included a larger number of questions, often five to ten. While individual questions were not always conceptually harder, they were more detailed and time-consuming. Like live IDE interviews, take-home tests were graded subjectively, with structure, explanation, and assumptions playing a larger role in the final evaluation.

Across all three formats, the testing method directly shaped what “good performance” meant. Timed portals rewarded fast and precise solutions. Live and take-home formats placed additional weight on how answers were structured, explained, and adapted to vague or imperfect problem statements. This difference in evaluation context becomes important when interpreting which SQL metric patterns appear most frequently in interviews.

How SQL Answers Were Evaluated

The way SQL answers were evaluated varied significantly depending on the testing format. In timed portal assessments, grading was typically binary. Queries either produced the expected output within the constraints of the platform or they did not. There was little room for partial credit, alternative approaches, or explanation. In these settings, correctness and speed outweighed all other considerations.

Live SQL interviews and take-home assignments were evaluated differently. In both cases, answers were rarely judged as simply right or wrong. Instead, evaluation fell on a spectrum that accounted for how the solution was constructed, how assumptions were handled, and how clearly the reasoning was communicated. Interviewers often looked for signals beyond the final query, such as whether the candidate understood data grain, handled edge cases, or chose an approach that scaled reasonably.

In several interviews, I encountered grading schemes that resembled informal scoring rather than strict pass or fail. A solution that met the basic requirements might be considered acceptable, while stronger performance required demonstrating an alternative approach, discussing trade-offs, or identifying potential issues with the data. In some cases, two candidates could arrive at correct results but be evaluated differently based on how well they explained their decisions.

Another factor was that SQL performance was not always evaluated in isolation. Some interview processes included multiple technical rounds, and final decisions were made across all of them. A strong SQL interview could be offset by weaker performance elsewhere, and the reverse was also true. Because of this, it was often difficult to determine whether a rejection reflected SQL performance specifically or the overall interview outcome.

Taken together, these evaluation differences explain why pass or fail outcomes alone are not a reliable way to assess SQL interview performance. They also help clarify why recurring SQL metric patterns are more useful to study than individual questions. The next section introduces the metric pattern framework used to categorize the interview questions in this analysis.

Brief Recap of SQL Metric Pattern Framework

In this analysis, I categorized SQL interview questions using the SQL Metric Pattern framework introduced in an earlier article on metric-driven SQL problem solving. That framework groups SQL questions by the type of metric being calculated rather than by specific SQL features or functions.

The patterns used in this article are:

• KPI – single-value metrics that summarize overall performance
• Breakdown – metrics segmented by one or more dimensions
• Ratio – metrics formed by dividing two related aggregates
• Rank – ordered metrics within a group
• Cumulative / Running Total – metrics that accumulate over time
• Moving Average – metrics smoothed across a rolling window
• Percent Change / Growth – metrics comparing values across periods

Grouping questions by metric pattern makes it easier to identify what interviews consistently test: not syntax or isolated SQL tricks, but the ability to define, compute, and reason about business metrics.

A full explanation of the framework, including examples and common pitfalls, is covered in the original article:
Sales Growth Dataset Exploration – Using the Data Analyst Cheat Sheet on Real Sales Data.

With this framework in place, the next section reports how often each metric pattern appeared across the interviews.

Results: Interview Sample and Question Distribution

This analysis is based on 11 data analyst interviews conducted between November 2023 and April 2025 that included SQL technical questions. Only interviews with explicit SQL evaluation were included. Across these interviews, multiple SQL questions were asked, resulting in a total pool large enough to observe clear repetition in metric types.

Interview Distribution by Company Group

The interviews spanned a mix of company sizes and domains. To avoid overemphasizing individual companies, interviews were grouped by category.

Total interviews – 11

While the sample is limited and geographically concentrated, the repetition of SQL question types across different company groups suggests that the patterns observed are not isolated to a single category.

SQL Interview Questions by Metric Pattern

Each SQL question was categorized using the SQL Metric Pattern framework described earlier. The table below shows how frequently each pattern appeared across the interviews.

The distribution shows a strong concentration in a small number of metric patterns, with Breakdown and Ratio questions appearing far more frequently than all others. More advanced time-based and windowed metrics appeared rarely in this interview set.

The next section interprets these results and explains why certain metric patterns dominate SQL interviews.

Interpretation: Why Certain Metric Patterns Dominate SQL Interviews

The distribution of SQL metric patterns in these interviews shows a clear concentration around Breakdown and Ratio questions. This is not accidental. These two patterns represent the foundation of how businesses ask analytical questions and how data analysts are expected to reason about performance.

Breakdown questions appear most frequently because they test whether a candidate can move from a high-level metric to a segmented view. In practice, this means understanding how to define the correct data grain, apply joins without inflating results, and group metrics by relevant dimensions such as time, region, product, or user type. A correct Breakdown query demonstrates that the candidate understands not just SQL syntax, but how metrics behave when sliced across dimensions.

Ratio questions appear frequently for similar reasons. Ratios require careful alignment between numerator and denominator, consistent filtering, and awareness of edge cases such as division by zero or missing data. From an interview perspective, ratio metrics quickly expose whether a candidate understands what a metric represents or is simply aggregating columns mechanically. Even when the SQL is relatively short, the reasoning behind it is often not.

KPI and Rank questions appear less frequently but still consistently. Single-value KPIs test whether a candidate can define a metric clearly and aggregate it correctly, while ranking questions introduce ordering and partitioning concepts that are common in reporting but secondary to core metric construction.

More advanced patterns, such as cumulative totals, moving averages, and percent change, appear far less often in this interview set. These patterns typically rely on window functions and time-series logic, which are important skills but are not central to most day-to-day reporting tasks for data analysts. Their lower frequency suggests that interviews prioritize correctness and clarity in foundational metric logic over advanced SQL features.

Overall, the pattern distribution indicates that SQL interviews emphasize metric definition, segmentation, and comparison rather than syntactic complexity. Interviewers appear to use SQL as a way to evaluate how candidates think about data and business questions, not how many advanced SQL techniques they can apply in isolation.

Takeaways

This analysis is based on a limited sample of 11 Bay Area data analyst interviews, but the concentration of question types was consistent enough to surface clear priorities.

Breakdown and Ratio questions dominated the interview set. Preparation time is best spent on getting these right: defining metrics at the correct grain, joining tables without inflating results, and grouping data in a way that matches the business question. These skills are reinforced in foundational, reporting-focused courses such as SQL Basics, Standard SQL Functions, and SQL Basic Reporting on LearnSQL.com, where queries are framed around real metrics rather than isolated syntax.

Advanced metric patterns—such as running totals, moving averages, or growth calculations—appeared far less often. They are useful additions, especially for reporting-heavy roles, but they do not replace strong fundamentals. Courses covering topics like Window Functions, Recursive Queries, and SQL GROUP BY Extensions on LearnSQL.com make the most sense once Breakdown and Ratio patterns are already solid.

Overall, the results suggest that SQL interviews reward clarity in metric reasoning more than breadth of SQL features. Preparation that emphasizes core metric construction and correct use of joins aligns most closely with what appears in real interview questions.

Wrap-Up

This article analyzed SQL interview questions from 11 real data analyst interviews to identify which metric patterns appear most often in practice. Rather than focusing on isolated SQL techniques, the analysis grouped questions by the type of metric being calculated, revealing a clear concentration around Breakdown and Ratio patterns across companies and interview formats.

The results suggest that SQL interviews consistently emphasize metric construction and reporting logic over advanced SQL features. Interviewers appear to use these questions to evaluate whether candidates can define metrics correctly, segment them meaningfully, and reason about comparisons in a way that reflects real analytical work.

The SQL Metric Pattern framework and the Data Analyst Cheat Sheet offer a structured way to approach these problems by combining SQL technique, metric definition, and business context. For candidates who want to practice these skills repeatedly across different datasets and difficulty levels, access to a broad, structured set of courses matters more than jumping between disconnected resources. This is where an option like the All Forever SQL Plan fits naturally, as it allows long-term practice of both foundational and advanced metric patterns without having to optimize for individual course selection.

This article focuses deliberately on core patterns rather than interview strategy. A deeper analysis could further break down questions by testing format or explore common sub-patterns within each metric type, but the findings here already point to a clear conclusion: mastering foundational metric logic offers the strongest return for SQL interview preparation.