Build the intelligence layer for a market that has never had one.
DealerSignals is an early-stage data intelligence platform — and we are looking for people who want to build something original. If the idea of building an intelligence layer that maps a $10B+ software market from the ground up appeals to you, read on.
An early platform with a clear direction.
DealerSignals launched in 2026 as a signal-based market intelligence platform for the automotive SaaS ecosystem. We aggregate publicly observable digital signals from dealership websites across the United States, normalize them through a proprietary methodology, and publish structured adoption intelligence that serves SaaS vendors, dealer groups, and institutional investors.
We are at the stage where every person on the team has a direct, measurable impact on the platform. There are no layers of management between the work and its outcome. The decisions we make now about data architecture, product design, and market positioning will define what DealerSignals becomes.
We are not a large company and we are not pretending to be. What we are is a focused team working on a data problem that matters — building the independent intelligence infrastructure the automotive technology market has been missing for two decades.
If that context appeals to you, read on. If you are looking for a fully-formed corporate environment with established processes and defined career ladders, we are probably not the right fit at this stage.
What We Value
Intellectual honesty over comfort. We publish data that is accurate, not data that tells a convenient story. We hold the same standard internally — we want people who will tell us when something is wrong, not people who will tell us what we want to hear.
How We Work
Small team, documented decisions, async-first communication. We believe that good work requires deep focus, and we structure our environment to protect it. We document reasoning, not just conclusions.
Location
We are remote-first. We do not require physical presence. We do require availability that overlaps meaningfully with U.S. business hours and the ability to communicate clearly in writing.
What we are building and why it is hard.
Building a market intelligence platform from publicly observable signals is a data engineering problem, a classification problem, a product design problem, and a market development problem — all at once. Here is what that actually involves.
Signal Collection at Scale
Observing and recording digital signals from tens of thousands of dealership websites on a continuous basis. Building infrastructure that is reliable, efficient, and produces data at sufficient quality to publish with confidence scores attached.
Normalization and Classification
The raw signal environment is messy. Vendor names appear in dozens of formats. Technology categories have blurry edges. Our normalization system converts raw signal data into structured, comparable intelligence — and it has to be right at scale.
Platform and Product
Turning a structured dataset into an intelligence product that sophisticated buyers — vendor strategy teams, investor analysts, dealer group executives — find genuinely useful. This is a product design challenge as much as a technical one.
Taxonomy Maintenance
The automotive SaaS market evolves. New vendors enter. Categories merge or split. Our taxonomy has to reflect the market as it is, not as it was when we designed the initial classification system. Versioned, documented, continuously updated.
Intelligence Publishing
Translating signal data into written market intelligence — insights, reports, and analysis that provide context and interpretation to the underlying numbers. This requires both domain expertise and the ability to write with precision.
Market Development
Introducing a new category of intelligence product to buyers who have historically navigated with vendor-supplied data. Building trust with skeptical, sophisticated buyers who will scrutinize the methodology before they open their wallets.
Current openings.
We hire for capability and judgment, not credential and title. If you can demonstrate that you do excellent work that is relevant to what we are building, we want to hear from you regardless of your background.
Data Engineer — Signal Infrastructure
OpenResponsible for the collection, storage, and processing infrastructure that powers our signal dataset. You will design and maintain the systems that observe dealership digital signals at scale, manage data pipeline reliability, and ensure that raw data enters our normalization layer at sufficient quality.
Market Intelligence Analyst
OpenResponsible for translating our signal dataset into published market intelligence — written insights, quarterly reports, and briefing materials. You understand the automotive technology market, can write with precision and authority, and are comfortable working directly with data to identify the story it is telling.
Account Executive — Vendor Segment
OpenResponsible for acquiring and growing subscribers in our vendor segment — automotive SaaS companies using DealerSignals for competitive intelligence and market strategy. You will sell to strategy, BD, and executive buyers at companies that understand the value of independent market data and need to be shown how to use it.
Product Designer
FutureWe will be hiring a product designer as the platform subscriber base grows. If you have experience designing data-dense intelligence tools for sophisticated professional buyers, we would like to know you exist before we formally open this role.
Don't see your role listed?
If you believe you can contribute to what we are building in a way that is not captured by the roles above, send us a note. Describe what you do well, what kind of problems you want to work on, and why DealerSignals specifically. We read every message.
Send a General ApplicationPrinciples we actually use.
These are not values we wrote for a careers page. They are the operating principles that govern how we make decisions about data, product, and how we work with each other.
Data accuracy is non-negotiable.
We do not publish data below our confidence thresholds. We do not smooth over coverage gaps with interpolation. We do not present directional estimates as measured facts. If we do not know something with sufficient confidence, we say so. This standard applies to our product and to our internal analysis.
Methodology transparency is a commitment, not a differentiator.
We publish our methodology in enough detail to establish what we measure and how. We do this because our buyers are sophisticated enough to demand it, and because we believe it is the right standard for an intelligence platform. We do not hide behind proprietary process claims when transparency is possible.
Independence is structural, not cultural.
We do not take advertising. We do not accept sponsored content. We do not have vendor relationships that influence our data. This is an architectural decision — not a preference that can be overridden when it is inconvenient. We will not take money that creates a conflict we cannot disclose.
Small team, high individual impact.
Every person at DealerSignals has a measurable, visible impact on what the platform becomes. We do not add headcount before we understand what the problem is. We do not hire to manage headcount. The people who thrive here are people who want to own outcomes, not occupy roles.
Ready to work on something original?
Tell us who you are and what you want to build. We respond to every application personally.
Contact Us