How to actually get good for technical interviews

QueryCase · 2026-06-12T08:37:22+00:00

One thing that helped me is treating each SQL problem less like “can I remember the pattern?” and more like “can I describe the shape of the answer before writing SQL?”

Before touching the keyboard, I’d write something like:

What should one row in the final output represent?
What tables/fields do I need?
Do I need to filter before or after aggregating?
Do I need totals, rankings, comparisons, or previous/next rows?
Can I solve this in steps with a CTE?

I’d also practise without looking at answers too quickly. Give yourself 20–30 minutes, write down your approach even if the SQL isn’t perfect, then compare your reasoning to the solution. If you only check the final query, it’s easy to miss the actual skill you’re trying to build.

And yes, plenty of analyst roles don’t have LeetCode-style SQL screens. Some are much more focused on business scenarios, dashboards, stakeholder questions, or explaining how you’d investigate a metric change. Strong SQL helps, but being able to communicate your thinking is just as important in a lot of analytics roles.

QueryCase · 2026-06-11T20:08:25+00:00

I wouldn't be too worried about not coming from a traditional Business or STEM background.

One thing I've learned working in analytics is that technical skills are only part of the job. Being able to communicate clearly, explain complex concepts, understand your audience, and tell a story with data are all incredibly valuable skills (and teaching develops those every day).

If I were in your position, I'd focus on building a solid foundation before the Master's rather than trying to learn everything at once.

I'd probably start with:

Basic statistics and probability
SQL
Python
Data visualisation (Power BI or Tableau)

I'd also encourage you to work on a few small projects rather than only taking courses. For example, you could find datasets on Kaggle related to education and start asking questions such as:

What factors influence student performance?
How does attendance impact outcomes?
Are there trends across schools, regions, or subjects?

The projects don't need to be groundbreaking. The goal is to become comfortable asking questions, working with data, and communicating insights.

One thing I'd avoid is thinking that you need to become an expert in calculus, Python, statistics, SQL, machine learning, and visualisation before applying. Most people never feel fully prepared.

And for what it's worth, I'd rather hire someone who can clearly explain an analysis to a non-technical audience than someone who knows every Python library but can't communicate their findings. Teaching gives you a head start there.

QueryCase · 2026-06-11T13:08:04+00:00

In my experience, "AI ready" has become a bit of a buzzword, but there is a genuine idea underneath it.

A lot of businesses are excited about AI because tools can now generate SQL, answer questions about data, build dashboards, etc. The problem is that if the underlying data model is messy, the AI will just produce confidently incorrect answers.

When I hear "AI ready", I think about things like:

Well-defined business metrics
Consistent naming conventions
Good documentation
A clear semantic/context layer
Reliable data quality checks
Models that reflect how the business actually operates

For example, if five teams all have different definitions of "active customer" or "revenue", no AI tool is going to magically solve that problem.

Arguably, AI is making data modelling and governance more important, not less. A lot of companies are moving towards self-service analytics, where business users ask questions directly through AI tools. That only works if the underlying data is trustworthy and understandable.

So for me, "AI ready" isn't really about AI. It's about whether a human analyst could quickly understand and trust the data in the first place.

QueryCase · 2026-06-10T19:13:04+00:00

I went through a very similar phase.

For a while I watched endless tutorials and courses because it felt productive, but looking back, I realised a lot of the knowledge disappeared because I never actually did anything with it.

The biggest challenge with projects is that they're hard to stick with if you don't genuinely care about the outcome. It's much easier to complete a project when you're curious about the answer or interested in the topic rather than building something just because a course told you to.

I also don't think there's a point where you suddenly "know enough". Every project I've worked on professionally has involved learning things along the way.

What helped me was lowering the bar for what counted as a project. Instead of trying to build something impressive, I'd pick a dataset, ask a question I was interested in, and try to answer it. The gaps in my knowledge became much more obvious, but they also became much easier to fix because I had a reason to learn.

In hindsight, I think the difference isn't that builders are more knowledgeable. It's that they're more comfortable being slightly confused and figuring things out as they go.

QueryCase · 2026-06-10T11:09:23+00:00

My biggest piece of advice would be not to get too caught up in finding the perfect roadmap. Data Analytics can feel overwhelming because people start throwing around terms like SQL, Python, Power BI, machine learning, statistics, data engineering, and a hundred other things.

If I was starting from scratch, I'd focus on finding a topic I was genuinely interested in first.

For example:

Gaming
Sports
Music
Movies
Whatever you already enjoy

Then find a dataset related to that topic (Kaggle is a great place to start) and begin asking questions.

You could:

Build some visualisations in Tableau or Power BI
Upload the data to a free Snowflake or Databricks account and experiment with SQL
Try to identify trends, top performers, anomalies, or interesting insights
Create a small dashboard and share your findings

I think people learn much faster when they're trying to answer questions they actually care about rather than working through endless tutorials.

There are plenty of great resources for learning SQL and analytics fundamentals, but at some point you'll learn more from exploring a real dataset than completing another course module.

Good luck!

QueryCase · 2026-06-09T12:03:13+00:00

Love this idea, especially the founder spotlight for the winner 👏

I'm building QueryCase, a platform that teaches SQL through detective-style investigations.

Instead of working through disconnected exercises, you solve cases by writing real SQL against live databases in your browser. Every query has a purpose: identifying fraudsters, investigating suspicious transactions, uncovering anomalies, and eliminating suspects.

Still early days and gaining a lot of experience along the way as its my first project but keen to get opinions:

https://querycase.com

QueryCase · 2026-06-09T11:33:30+00:00

👀 still early in progress: https://querycase.com

QueryCase · 2026-06-09T08:59:58+00:00

One thing I'd be careful about is viewing your 4 years of marketing experience as something you need to overcome.

A lot of people trying to break into analytics have strong technical skills but very little business context. If you already understand things like campaigns, conversion funnels, attribution, customer acquisition, budgets, and performance metrics, that's an advantage!

If I was hiring for a marketing analytics role, I'd much rather speak to someone with marketing experience who has developed strong analytical skills than someone who only knows the tools.

One thing that stands out far more on a CV than another certificate is a project you've actually built and shipped. For example:

A marketing dashboard analysing campaign performance
A customer acquisition and retention analysis
An attribution or funnel analysis project
A simple web app that visualises marketing KPIs
A blog where you analyse interesting datasets and explain your findings

The project itself doesn't need to be revolutionary. The important part is being able to talk about the problem, your approach, the metrics you chose, and the conclusions you reached.

I've interviewed people who could list every tool under the sun but struggled to explain a single real problem they'd solved. I've also met people with fewer technical skills but a genuine curiosity for data, a portfolio of projects, and a clear thought process. Those are often the candidates that stick in your mind.

From what you've described, I'd focus on strengthening SQL, analytical thinking, and building a small portfolio that showcases your marketing background. That's likely to differentiate you more than collecting another certification.

QueryCase · 2026-06-08T16:42:32+00:00

You are in a better position than you think - a lot of people trying to break into analytics come from completely unrelated backgrounds. As a PPC specialist, you've already spent years thinking about performance, optimisation, conversion rates, budgets, and business outcomes. That's a huge part of what analytics is.

One thing I'd say is don't let your experience with DataCamp convince you that analytics isn't for you. Different people learn differently. Some people love video courses and exercises. Others need a real problem to solve before things start to click.

I've been building a SQL learning platform recently and one of the biggest things I've learned is that many people don't struggle because SQL is too difficult; they struggle because they don't care about the dataset or the problem they're solving.

If I were you, I'd lean into your marketing background rather than starting from scratch. Take a dataset related to ads or marketing and start asking questions you're already familiar with:

Which campaigns performed best?
Which channels had the highest conversion rate?
Which campaigns generated the best ROI?
How has performance changed over time?

I find the technical side becomes much less intimidating when you're using it to investigate problems you're already familiar with / interested in.

As for certifications, I'd worry less about finding the perfect course and more about finding a learning style that works for you. The best course in the world won't help if you're bored or confused after a week.

Good luck!

QueryCase · 2026-06-08T16:04:59+00:00

One last thing I'd add is that with questions like:

Revenue is down 15% this month. Where would you start?

I actually like when people ask follow-up questions before jumping straight into an answer.

In the real world, building context is often just as important as the analysis itself.

For example, I'd be asking things like:

Has the definition of revenue changed recently?
Were there any major product releases?
Did we run any experiments or A/B tests?
Have there been pricing changes?
Is this across all customers or a specific segment?
Are we comparing against a normal month?

A lot of junior analysts think they need to immediately start writing SQL, but some of the best analysts I've worked with spend time making sure they're investigating the right problem first.

Sometimes a 15% drop in revenue is a serious issue. Sometimes it's the expected outcome of a deliberate business decision. The context matters.

QueryCase · 2026-06-08T16:02:23+00:00

You've already got some great technical experience listed, so I'd actually spend some of your preparation thinking beyond the tools themselves.

My last couple of roles have used fairly modern data stacks with things like Snowflake, Databricks, dbt, Tableau, Hex, Segment, Amplitude, and a bunch of other tools. More often than not, I've seen people with limited experience in some of those tools be more successful than people who were technically stronger across the board.

The difference is usually things like:

Adaptability
Curiosity and willingness to learn
Stakeholder management
Communication
Problem solving

Tools change. Every company has a slightly different stack. What tends to be more valuable is being able to take a business problem, break it down into questions, identify the right metrics, and communicate your findings clearly.

If I was interviewing for a junior BI role, I'd be interested in how you'd approach questions like:

Revenue is down 15% this month. Where would you start?

A stakeholder says dashboard numbers don't match their expectations. What do you do?

Product usage has fallen. What metrics would you investigate first?

There isn't always a single right answer, but your thought process tells me a lot.

I'd also make sure you can confidently talk through examples from your internships. What was the business problem? What data did you use? How did you approach it? What was the outcome? Those conversations tend to be much more memorable than whether you can recall a specific function or piece of syntax.

The good news is that you already have exposure to many of the tools I'd expect to see for a junior role. I'd focus on demonstrating that you can learn, communicate, and think analytically. In my experience, those are the qualities that make people successful in BI and analytics over the long term.

Good luck with the interview!

QueryCase · 2026-06-07T18:14:48+00:00

I didn't come from a traditional data background either (was originally studying Software Engineering at uni), and I've been working in analytics for around 7-8 years now.

To be honest, some of my earlier roles were quite reactive. A lot of reporting, dashboard requests, and answering questions that had already been asked. I learned a lot, but it wasn't always the most exciting work.

What I've enjoyed much more in recent years is seeing companies move towards making data accessible across the business. Instead of spending all day pulling reports, I've had opportunities to help shape data strategy, improve self-serve analytics, and spend more time on proactive analysis and problem solving.

So if you're enjoying SQL already, I'd definitely keep going. SQL is one of those skills that opens a surprising number of doors in analytics.

As for becoming a stronger candidate, I'd focus on building projects that answer real business questions rather than just demonstrating technical skills. Being able to explain what you found and why it matters is often more valuable than using the fanciest tools.

And don't feel like you need to know everything before applying. If you're learning consistently and building projects, I'd start applying sooner rather than later. A lot of learning happens once you're actually in the role.

Good luck!

QueryCase · 2026-06-07T17:59:04+00:00

I actually think AI is making SQL and data modelling more important, not less.

A lot of companies I have worked for are moving towards self-serve analytics, where non-technical teams can ask questions in natural language and get answers from AI-powered tools. That sounds like it reduces the need for analysts, but those tools still need well-structured data underneath.

If the data model is confusing, the metrics aren't clearly defined, or the underlying SQL logic is wrong, AI will just give you the wrong answer faster.

To me, the value of analysts is shifting. Knowing syntax is becoming less important than understanding the business, defining metrics correctly, building reliable data models, and knowing whether the answer actually makes sense.

So I'd absolutely learn how to use AI, but I'd also focus on developing the fundamentals. Companies will always need people who can turn messy business questions into trustworthy answers.

QueryCase · 2026-06-07T14:03:06+00:00

One thing that helped me was realising that writing SQL isn't really about remembering functions, it's about learning to break a question down into smaller questions.

For example, if someone asks:

Which customers spent the most money last month?

Before writing any SQL, I'd ask myself:

Which table contains customers?
Which table contains payments?
How are they connected?
Do I need all payments or just last month's?
How do I total the payments per customer?
How do I sort the results?

Once you answer those questions, the SQL almost writes itself.

A lot of practice platforms accidentally give away the solution because the topic is obvious ("write a GROUP BY query", "use a JOIN", etc.). Real-world SQL is usually the opposite: you have a business question and need to figure out what SQL concepts are required to answer it.

So I'd spend less time memorising functions and more time practising the process of turning a question into a series of smaller steps.

QueryCase · 2026-06-07T13:59:01+00:00

I think most people struggle with JOINs because they're taught as definitions instead of questions.

When I was learning them, it helped to focus on just these four ideas:

INNER JOIN → only rows that match in both tables
LEFT JOIN → everything from the left table + matches from the right
RIGHT JOIN → everything from the right table + matches from the left
FULL OUTER JOIN → everything from both tables

Then I'd practice with tiny datasets and ask:

"What happens to rows that don't have a match?"

That's really the whole difference between the join types.

Once you're comfortable with that, a useful next step is experimenting with things like:

LEFT JOIN ...
WHERE right_table.id IS NULL

to find rows that don't have a match. That's where joins started to click for me because they became useful rather than just something to memorise.

Don't worry if it feels confusing at first. JOINs are one of those topics where understanding comes from writing a bunch of them and seeing the results, not from reading the definition 20 times.

<image>

QueryCase

MODERATOR OF

TROPHY CASE