Member of Technical Staff: Data
- Market p25–p75
- $200k–$270k
- Opened · 30d
- +0
- Closed · 30d
- −0
- Opened · 7d
- +4845% of open
- All software engineering
- 75,164open
Member of Technical Staff: Data
Monk is an AI-native accounts receivable (AR) platform for B2B companies, helping businesses get paid fast.
The AR stack is broken. Finance teams still chase payments with spreadsheets, reconcile AR in spreadsheets and use software that was built a decade ago. We think this is one of the largest, most ignored problems in B2B software. Trillions of dollars sit in outstanding invoices and the tools to unlock cashflow are stuck in the past. We are rebuilding Monk with AI, speed, and real craftsmanship.
We are already doing this for over $1B of accounts receivables. We are backed by leading investors and our founders bring significant experience to our company (more here).
We are early, the upside is enormous, and the hardest, but also the most rewarding work is ahead of us.
The role
You will be Monk's first dedicated data hire owning our full data stack – from architecture and pipelines to telemetry, analytics and beyond. Monk agents run on data, and ours has to be correct every time. You will build and maintain the systems that keep our data accurate, reliable, and actionable within the company. This is a one of a kind opportunity for the data-obsessed engineer.
In this role you will
Design and manage database ingestion, architecture, data modeling
Be the end-to-end owner for ETL/ELT pipelines and orchestration workflows
Build best-in-class monitoring, validation, and alerting for your work
Deliver world-class reporting for customer-facing agentic products and internal teammates: metrics, datasets, self-service analytics
Propose and ship new data integrations with internal and external systems
Who you are
4+ years building production data systems at a venture-backed startup as a data engineer full-stack data person.
You scaled the system you built, and lived with the consequences of your own choices
Expert in Postgres and SQL (must have)
Expertise in data pipelines, ETL/ELT tools, warehouse architecture
Hands-on with Supabase, AWS, Vercel, or comparable modern cloud infrastructure
An owner. You do not file tickets about pipeline failures. You fix them, then make sure they cannot happen again
AI-native. You use AI to move faster and are excited to build for agents.
Excited to build in person in Flatiron, NYC
Bonus if you
Have experience with fintech or payments data systems
Have built data infrastructure for LLM or agent products
Were a founding or early data hire
Our values (we hope that you will add your DNA/values and impact our culture as you build with us)
Ownership and craft: We own our work end to end and obsess over getting the details right. We believe that our Customers deserve the best possible outcomes and we strive to produce work that would get a 12/10 reaction from either our Customers or members of our team. Ownership is also knowing when to go deep on a problem and when to move fast. And when the v1 is live - we own all follow-through until it is perfect
Speed and urgency: We ship daily. We act now. Not tomorrow and not the end of the week, but today - this hour. We love shipping fast to delight our Customers
Radical clarity: We believe in getting to the truth. It is important that all of us reason from the same version of reality (the truth) so that we can get the best result. We strive to get the truth by any means necessary. This means being open to feedback, obsessing over improving from our mistakes and clear communication
Why solve this problem now
A broken market and a real shot at owning it. AR is huge, ignored, and ready to be rebuilt. We're early and the surface area is enormous
$T+ market and max EV from a single AR wedge
No clear winner has emerged in this massive category. It can be us
Insulated from labs. This workflow is complex, too many integrations are required and the last mile in AR is too bespoke for general purpose models
Hard problem worth solving. Revenue intelligence is challenging and inherently unsexy. Frankly, there are fewer strong companies in our space and we are building the most talent-dense team