Technical Business Analyst

Revyse; United States; On-site; USD 90,000 - 130,000; Full time

Job summary

Revyse is seeking a data-oriented analyst to optimize AI-powered engineering workflows. You will collaborate with product managers and engineers to capture context, write clear documentation, and surface data-driven insights.

You will use SQL against real data to quantify impact, maintain dashboards, and build quick prototypes that communicate ideas before full implementation. This role blends analysis, prototyping, and clear written input.

Qualifications

• 3+ years as a business analyst, technical analyst, product analyst, data analyst, or product operations.
• Strong SQL. You write your own queries against a real schema, understand joins, and sanity-check results.
• You can read a database schema and infer product behavior from it.
• Daily use of AI as a tool; you structure context for reliable output and spot confidently wrong models.
• You can build a rough working prototype with AI assistance, not production code.

Responsibilities

• Context & Documentation: document platform behavior, workflows, and rules; maintain accurate reference material.
• Product Data & Analysis: answer product questions with SQL, quantify shipped work, maintain recurring reports.
• Prototyping to Communicate: build rough prototypes to communicate ideas and hand intent to engineers.

Skills

SQL

Read DB schema

AI tooling

Prototype creation

Clear writing

Engineering collaboration

Job description

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.

How We Build (Read This First):

We don’t run sprints. Our company prioritizes the work together. Engineers work in cross-functional pods. AI drafts a lot of our tickets, and Engineers write their own. There is no process layer standing between an idea and the code.

That model moves fast, and it is only as good as the context feeding it. A confident, well-structured ticket that is wrong about how insurance requirements vary by trade can drastically slow us down. An engineer picks up the most legible piece of work rather than the most valuable one, because the valuable one was never made easy to understand.

This role’s north star is to make the right work the easiest work to pick up.

What You’ll Own:

1. Context & Documentation

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.

2. Product Data & Analysis

You’ll answer product questions with SQL against real data. Things like usage, throughput, drop-off, exception rates, workflow completion, turnaround times.

Quantify shipped work. Did it move the number it was supposed to move, and by how much.

Build and maintain the recurring reporting the pods rely on, so nobody rebuilds the same query every month.

Surface what nobody asked about. Note the distribution, the outliers, and the places where two numbers should reconcile and don't. The anomaly is usually the real finding.

3. Prototyping to Communicate

A working prototype settles an argument that a document might extend. You will build them constantly, and engineers will rebuild the real thing.

Use AI tooling to build rough, working versions of an idea. A screen, a script, a query tool, a data view, so the team can react to something concrete.

Build small internal tools for yourself and the team where doing so is faster than asking for one.

Hand intent to engineers clearly. Your prototype is the argument, not the implementation.

What This Role Is Not:

You will not be asked to own roadmap, prioritization, or customer commitments, and we will not quietly hand them to you.

You are also not asked to be an engineer. You prototype to communicate; engineers own what ships.

What You Bring:

• 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.

How This Role Works Here:

Worth knowing before you apply, because you will meet all of it in your first month.

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!

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.

Why Join Team Revyse:

• You will work the way most teams are still writing memos about. No sprints, no ticket bureaucracy, AI in the actual workflow rather than in a pilot program.
• Your analysis will change decisions. On a team this size, the person who brings the number to the conversation shapes the outcome.
• Multifamily vendor compliance is unglamorous and quietly determines how well hundreds of thousands of residents get taken care of. The problems are real and the data is genuinely interesting.
• We value each other as individuals and are energized by the idea of understanding our colleagues on a deeper level.
• We believe that 100% of the organization should understand and be accountable to the vision and strategy. If we all know where it is we’re trying to go, it’s far easier to row the boat in unison.