Your First Finance-Focused SQL Query

From curiosity to insight—in just a few lines of code

If you’re reading this, you probably already know that SQL is a valuable skill in finance. But knowing why isn’t the same as knowing how. So let’s change that—right now.

In this post, I’ll walk you through your very first finance-focused SQL query. You’ll see exactly how to ask a business question using SQL and get a clean, reliable answer in seconds. No programming experience required.

Let’s go!

The Scenario: Revenue by Product Line

Imagine your manager asks:

“Can you tell me total revenue by product line for last quarter?”

It's a simplified example but let's assume that this data is not readily available from a BI dashboard. If you’re in Excel, this means:

• Downloading the right data
• Filtering by dates
• Creating a pivot table
• Checking for missing data
• Copy-pasting for slides or dashboards

Now imagine doing that with one query, always up to date, straight from the source system, without having to involve a BI developer.

The SQL Query

Here’s how we’d ask that same question in SQL:

SELECT
  product_line,
  SUM(revenue) AS total_revenue
FROM
  finance.sales_data
WHERE
  invoice_date BETWEEN '2024-10-01' AND '2024-12-31'
GROUP BY
  product_line
ORDER BY
  total_revenue DESC;

Breaking It Down

The first thing you will notice is how close this is to plain English. It's very readable even if you have never seen SQL before. Follow that tangent for a moment before diving into each part of the query, because the readability is another of SQL's strengths compared to spreadsheet formulas: because references to data and operations on it have descriptive names, it's easier to follow than the often obscure spreadsheet cell references, and even though it's avoidable many finance professionals (perhaps you have a colleague like this!) almost seem to take pride in writing "clever" formulas. I've seen something along these lines way too often:

=IFERROR(SUMIFS(FILTER(C2:C847, (A2:A847>=E1)*(A2:A847<=E2)*(D2:D847="External")), B2:B847, G2, E2:E847, H2, F2:F847, I2), 0)

Now let’s go through the SQL line by line:

• SELECT – This defines what you want to see in the result: product_line and the sum of revenue.
• FROM – This tells SQL where to find the data. In this example, it’s in the finance.sales_data table, which we assume is available in your data warehouse.
• WHERE – A filter to focus only on the relevant time period (last quarter).
• GROUP BY – This tells SQL to group all matching rows by product line before summing the revenue.
• ORDER BY – This sorts the results so the highest revenue product lines appear first.

That’s it. No macros. No dragging formulas. And in a later tutorial we will show how to make date ranges dynamic, meaning that you can run the script

What You’ll See

When you run the query, you’ll get a table like this:

Clean, structured, and ready to visualize or automate.

Try It Yourself

If you have access to BigQuery (or another data warehouse), try running the query in your browser, making sure to adjust the column names (revenue, product_line, invoice_date) based on your company’s schema. Don’t have access yet? Ask your BI or data team for read-only access to a safe dataset like sales orders or invoices.

What You Just Learned

With just a few lines of SQL, you now know how to:

• Select specific columns
• Filter by some criterion such as date
• Group by a business dimension
• Aggregate values with SUM
• Sort your results for clarity

This single query pattern can power 80% of the reporting needs finance teams face every day.

What’s Next

Next, we’ll look at how to join multiple datasets—for example, linking actuals with budgets, or finance data with operational KPIs.

If you’re following this learning path, you’re already ahead of the curve. Just keep going!