Lakshya

Legal, Finance & People

FP&A / Strategic Finance

Business · the numbers behind the plan

The person who has to say what a decision costs, and who is judged on whether the forecast was close.

The person who has to say what a decision costs, and who is judged on whether the forecast was close.

A day that is actually representative

TimeWhat you are actually doing
09:00Month-end close variance. The interesting part is which assumption broke, not the number.
10:30A business case for a hiring plan, where the honest version has a range not a point.
13:00Unit economics — CAC, payback, gross margin by segment, and where the model is wrong.
15:00A cost conversation with engineering about cloud spend, which needs you to understand it.
16:30Board-pack numbers, where the commentary matters more than the table.

The decisive round

“Cloud spend is up 40% and engineering says it's necessary.”

Interviewer: “How do you approach it?”

A weak answer. “I'd ask engineering to justify the increase and look for opportunities to reduce costs, and set a budget target for the next quarter to bring spend back in line.” An adversarial posture and a target set without a mechanism. It also produces the classic outcome where engineering cuts the cheapest thing to cut, which is usually a non-production environment nobody needed to cut.

A strong answer. “My starting assumption is that engineering is probably right that it's necessary, and also that nobody knows which 40% is the necessary part. Those are compatible, and that's the useful frame — it turns an argument into a shared question.

The first thing I'd want is spend per unit of business, not spend in rupees. If revenue grew 50% and infrastructure grew 40%, that's operating leverage improving and I should say so plainly rather than flagging it as a problem. If revenue grew 5%, that's a different conversation entirely. Absolute cloud growth is almost never the right metric and finance teams flag it constantly.

Then attribution, which is where this usually falls apart. Most companies can't tell you cost per customer or cost per product line because nothing is tagged. So I'd work with engineering on tagging — not to police them but because they usually want it too; they just have never had a reason strong enough to prioritise it.

Then I'd look at the shape. Typically some is genuine growth, some is a specific product that is much more expensive than we priced it, and some is waste — idle non-production, over-provisioned instances, old snapshots. The third is real and it is rarely 40%.

The finding that changes decisions is usually the second one: a product line whose gross margin is far worse than the blended number suggests. That's a pricing conversation, not a cost conversation, and it's worth more than any efficiency work.

And I'd give engineering the visibility rather than a target. Teams that can see their own spend reduce it without being asked; teams handed a target cut whatever is easiest to cut.”

Unit economics rather than absolute spend, attribution before action, and reframing the finding as a pricing question. Giving visibility rather than a target is the FinOps insight that separates a partner from a controller.

What to build or bring

  • A forecast and its accuracy, including one you got wrong and why.
  • A business case that changed a decision.
  • Unit economics you built — CAC payback, margin by segment, cohort retention.
  • Enough technical fluency to have a real conversation about cloud cost. Rare in finance and highly valued.

Compensation

MarketBandNotes
United States$110k – $190kHigher at pre-IPO companies
India₹15L – ₹45LLarge market; strong in GCC finance centres

The book for this field

Legal, Finance & People

Business functions inside a technology company: commercial counsel and legal operations, technology finance and SaaS metrics, recruiting and people operations — and why all three are now systems-building jobs.

CHAPTER 3Finance in a technology businessSaaS metrics, revenue recognition, and why the finance analyst is expected to write SQL.READ THE CHAPTER →CHAPTER 1Business functions inside a technology company632 postings. Scaling in 93%, data and analytics in 91%, AI in 84% — these are not generic corporate roles.READ THE CHAPTER →CHAPTER 2Commercial counsel and legal operationsThe largest cluster here, and 'legal engineer' is now a title in its own right.READ THE CHAPTER →

All 4 chapters in Legal, Finance & People →

Cross-cutting

Skills every archetype tests

These are shared across every field on this site — the same question asked in different vocabulary — so preparation here compounds rather than being spent once. The book above is specific to your field.

Measuring what resists measurementTested in every field on this site under a different name — evals, SLOs, DORA, ablations. Named in 59% of AI and 42% of platform postings, and almost nobody studies it deliberately.READ THE CHAPTER →The controlOne habit separates strong candidates from plausible ones more reliably than any technical depth.READ THE CHAPTER →Latency and cost arithmeticThe one piece of maths that is genuinely counterintuitive — and it comes up in every agent interview.READ THE CHAPTER →

Practice the 10 Business questions →  ·  Back to Legal, Finance & People →