Technical Business Analyst

Revyse Job Opportunity

We provide cost-effective coverage with just a few clicks — satisfying residents, property managers, agencies, and distribution partners with a process that is as simple and touchless as possible.

Revyse helps multifamily operators discover the best vendor partners, manage contracts and compliance, and reduce financial risk. Our platform turns vendor data into a strategic advantage - and our newest compliance product is changing how property management companies onboard, verify, and support vendors.

We're a fast-moving post-acquisition startup with a big vision: overhaul how operators and suppliers work together. Founded by industry experts and backed by leading multifamily investors, Revyse is growing quickly - and we're looking for someone who loves building order from the chaos of growth.

About the Role

You support pods, not a single manager's queue. Product managers and engineers are both your customers, and they will want different things on the same day. So will real customers!

You have no authority to assign work. Nobody here does. Your influence comes entirely from the quality of your context and the clarity of your case, which is either the best part of this job or the wrong job for you.

The domain has depth and complexity. Insurance requirements vary by trade, scope of work, and state. Compliance rules vary by customer. You will not be able to reason about our data without learning the domain, and we will give you time to learn it.

Our documentation is uneven. Some things are written down. Many are not, and finding out is the work rather than a blocker to it.

We are a small company post-acquisition and still forming. Your work is visible, and so are the gaps you close.

What You'll Do

If AI drafts the tickets, the quality of what gets built is decided by the context it receives. That context is what you'll optimize.

Understand and write down how the platform actually behaves today - the workflows, the exception paths, the rules, and the undocumented behavior currently living in people's heads.

Build and maintain the reference material that our AI tooling and our engineers pull from, and keep it accurate as the product changes. Stale documentation now produces bad tickets and bad code automatically, at scale.

Review tickets and specs against reality before anyone builds them. This is the part that matters most: AI-generated work is confidently wrong exactly where it costs the most - edge cases, compliance rules, customer-specific commitments, anything that is not in the repo or the training data.

Capture acceptance criteria, edge cases, failure behavior, and explicit non-goals so a pod can pick something up and build it without a meeting.

Qualifications

3+ years as a business analyst, technical analyst, product analyst, data analyst, or in product operations, working closely with engineers.

Strong SQL. You write your own queries against a real schema, you understand what a join is doing to your row count, and you sanity-check your results before presenting them.

You can read a database schema and work out how a product behaves from it.

You already work with AI as a daily tool, not an experiment. You know how to structure context so output is reliable, you iterate on prompts rather than accepting the first answer, and you know when a model is confidently wrong and you check.

You can build a rough working thing with AI assistance. Not production code. A prototype good enough that people can react to it instead of imagining it.

You write clearly. In a setup like ours, written clarity is not a soft skill. It is the input that determines what gets built.

Analytical honesty. You quantify rather than characterize, you name your assumptions, and when numbers don't reconcile you stop and investigate instead of shipping the chart.

Comfort operating without process scaffolding, and comfort saying "not this week, here's why" when three people want the same hour.

• Bonus: B2B SaaS with enterprise customers, compliance- or workflow-heavy products, Python for analysis, or experience maintaining documentation that AI tooling depends on.