The 3 business metrics every senior data engineer must know

A crash course in business fluency for data engineers who want to lead teams, influence decisions, and stop getting lost in executive meetings.

Hey fellow data nerd, Yordan here,

If you’ve been working in data for a while, you already know how to build reliable pipelines. You know how to scale infrastructure, manage messy schemas, and automate reporting for every team in the company. That’s what got you here.

But now you’re being asked to do something different:

• to “align with the business.”

• to show “impact.”

• to influence strategy.

And here’s the problem: most engineers, especially those who came up through technical tracks, have never been taught how to read a business conversation, let alone contribute meaningfully to one.

You and I nod through terms like “CAC” and “ARR,” then quietly Google them later.

That’s not your fault. But it is your responsibility to fix.

If you want a seat at the table where decisions are made, where product priorities are set, budgets are debated, and long-term bets are placed, then you need to speak the language. That language is built around a handful of metrics.

In this article, I will teach you three of them.

These are the metrics that drive revenue, guide investment, and expose real risk. Once you understand how they work, and how your work connects to them, you’ll never look at your role the same way again.

Let’s imagine a business you work for

To make this practical, let’s ground everything in a real-world (imaginary-ish) setting.

You’re the senior data engineer at a retail company. It sells shoes and apparel online and in 50+ physical stores. That part’s straightforward.

But here’s the twist: the company also runs a paid loyalty program.

Customers pay $10/month for perks like free shipping, early access to new product drops, and exclusive discounts. That recurring subscription is where a big chunk of the company’s revenue comes from. And it’s growing fast.

As the business matures, the leadership team wants to double down on this model. They want retention. Predictable revenue. Scalable growth. And they want the data team (that’s you) to help make it happen.

Suddenly, your dashboards aren’t only reporting performance. They're being used to make funding decisions.

That’s why you need to understand business metrics. Let’s start with the most fundamental one.

Metric #1: Monthly recurring revenue (MRR) shows how much predictable income the company generates

If the company you work for sells subscriptions, or anything with recurring payments, then MRR is the number everyone watches. It’s not a vague trend or a high-level guess. It’s hard cash coming in each month, on a repeatable schedule, from paying customers.

That’s what makes it powerful: it reflects the financial engine of the business in real time.

What exactly is MRR?

Monthly recurring revenue is the total amount of predictable revenue generated from active subscriptions in a given month.

It doesn’t include one-time purchases. It doesn’t include future projections. It’s not influenced by seasonal spikes or one-off discounts. It’s about what’s expected, month after month.

For a loyalty program that charges customers $10/month, this is straightforward:

MRR = number of active paying users * average monthly revenue per user

A simple example

Let’s say your loyalty program has 20,000 paying members. Each one pays $10/month.

That gives you:

MRR = number of active paying users * average monthly revenue per user

That’s $200,000 in recurring revenue every month, as long as those members stick around.

Why this matters to data engineers

If you’re working on anything that affects signups, retention, upsells, or pricing tiers, you’re touching MRR.

That means your impact goes beyond tables and ETL jobs. If your onboarding optimization leads to a 10% increase in conversion, that’s not a “nice bump in metrics”. It's tens of thousands in new recurring revenue.

And that’s the language leadership listens to.

Related terms you’ll hear

• ARR: Annual recurring revenue. It's MRR × 12.

• ARPA: Average revenue per account. A more precise version of "ARPU" for teams that sell into businesses.

• EBITDA: A profitability measure that excludes debt, taxes, and depreciation. Less relevant day-to-day, but shows up in investor decks.

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Want to go deeper than definitions?

Understanding MRR is a start, but real business fluency comes from seeing how teams actually use these numbers to make decisions.

If you're serious about leading with data, I host occasional small-group coaching calls for paid subscribers, where we walk through real scenarios from reader questions:

• how do I trace MRR movement back to product events?

• what dashboards do execs actually care about?

• how should I model ARR when it’s booked annually but recognized monthly?

It’s part workshop, part conversation, and completely tailored to engineers looking to grow their strategic muscle.

You’ll get access as a paid subscriber.

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Metric #2: Customer acquisition cost (CAC) shows how much you spend to get one new paying customer

Every business dreams of growth. But growth that costs more than it brings in is a disaster waiting to happen. That’s what CAC reveals.

It tells the business: “Here’s how much it costs us, across ads, salaries, tools, and more, to convince one person to pay.”

What exactly is CAC?

Customer acquisition cost is the average amount of money the company spends to acquire each new paying customer.

CAC = total marketing and sales spend / number of new paying customers

This includes paid ads, affiliate deals, the content team’s salary, CRM tools, sales commissions, and everything else that goes into acquisition.

A simple example

Let’s say your company spends $100,000 on marketing in a month, and it brings in 1,000 new loyalty subscribers.

Then:

CAC = $100,000 / 1,000 = $100 per customer

That means you need each customer to stick around for at least 10 months (at $10/month) just to break even.

Why this matters to data engineers

At first glance, CAC might seem like a marketing metric. But think about what affects customer acquisition:

• is the signup experience smooth?

• is the data clean for attribution?

• does the system recommend the right product on the landing page?

• are activation events firing correctly?

Every one of these depends on the work of data engineers. You may not be placing ads, but your work defines how success is measured. And often, how efficient that success becomes.

Terms to pay attention to

• LTV (lifetime value): The total amount of revenue a customer generates before they churn.

• LTV:CAC: A profitability ratio. If LTV is less than CAC, you're losing money on every customer.

• Activation rate: The % of users who hit an early success milestone, like completing a first purchase.

• Payback period: How many months of revenue it takes to recover the acquisition cost. That's 10 months in our case.

Metric #3: MRR churn tells you how much recurring revenue the company loses each month

Churn is the metric no one wants to talk about, but the one every healthy business tracks obsessively.

It shows you how many paying customers walked away, or how much recurring revenue disappeared, since last month.

Most companies can grow for a while by acquiring new users. But if churn is too high, it eventually outpaces growth. You’re filling a leaking bucket.

What exactly is MRR churn?

It’s the percentage of monthly recurring revenue lost from cancellations, downgrades, or non-renewals.

MRR churn = (lost MRR / starting MRR) * 100

This helps teams understand whether their revenue engine is stable or eroding under the surface.

A simple example

At the start of the month, your MRR is $200,000. Over the next 30 days, 500 users cancel their subscriptions, and you lose $5,000 in MRR.

MRR churn = (5,000 / 200,000) * 100 = 2.5%

A churn rate of 2.5% might seem small. But if it continues month after month, it puts massive pressure on acquisition. You have to keep replacing lost revenue to stay even.

Why this matters to data engineers

Most churn issues aren’t caused by pricing. They’re caused by poor product experience.

Users churn when things are slow. When they don’t get value quickly. When features don’t work as expected. When no one notices they’re disengaging.

That’s where data engineers come in.

With the right telemetry, models, and experiments, you can detect early churn signals, personalize interventions, and help product teams design better retention loops.

Churn is more than a number. It’s a signal. And data can change its trajectory.

Final Thoughts

You don’t need to be a business expert. You need to be business-aware

If you’re a data engineer who wants to lead, these three metrics are your new compass:

• MRR shows the health of the revenue engine.

• CAC reveals how efficient your growth is.

• Churn tells you whether your progress is sustainable.

No one expects you to become a CFO. But if you want to shape product strategy, influence priorities, and own impact, not just infrastructure, you need to speak the same language as the people making those decisions.

Start with these three numbers. Ask how they’re calculated at your company. Follow the math. Then follow the money.

Because when you connect your work to revenue, you don’t just get invited to the table.

You help decide where the table goes.

Until next time,

Yordan