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Daloopa
Source-linked financial data infrastructure for AI and agentic investment workflows
Last verified August 17, 2026 · Updated daily
What Daloopa is building
Daloopa is the plumbing that makes AI actually useful in finance. The problem it solves is specific and consequential: when an AI agent produces a financial analysis, the answer is only as reliable as the data it retrieved. Most AI-accessible financial data is web-scraped, inconsistently labelled, and not traceable to an original filing. When the data is wrong, the analysis is wrong, and in investment decisions, that costs money. Daloopa's platform covers 5,500+ public companies globally. Every data point is extracted directly from the original source document (SEC filing, investor presentation, earnings release) and hyperlinked back to it. Fiscal calendars are normalised. Metric definitions are standardised across companies. Historical data goes back up to 14 years. The platform delivers 10 times more data points per company than competing providers. In a benchmark study, AI agent accuracy improved by up to 71 percentage points when grounded in Daloopa's auditable dataset versus web-based retrieval. Delivery is format-agnostic: Excel add-in for traditional analysts, API for programmatic access, cloud-native delivery via Snowflake, Databricks, and AWS S3 for enterprise data teams, and MCP connectors that plug directly into Claude, ChatGPT, Perplexity, and Rogo for agentic workflows. The Partner API launched recently allows third-party developers to build on the data layer directly. The product is not theoretical. 160+ financial institutions are paying customers, Anthropic and OpenAI use it, and the company has doubled revenue year-on-year. Fast Company ranked Daloopa #10 in the Emerging Enterprise category in its 2026 Most Innovative Companies list.
Why this matters
Investment research is one of the largest and most data-intensive white-collar workflows in the world. For decades, it has run on a manual process: analysts pulling numbers from filings, cleaning and reconciling them, building and updating Excel models, before getting to any actual analysis. AI has been able to do the analysis part for two years. The bottleneck is the data input: if the figures going in are wrong, the analysis coming out is wrong. The AI in financial services adoption curve makes this urgent. The Cambridge Centre for Alternative Finance's 2026 Global AI in Financial Services Report found 81% of surveyed firms are now adopting AI, with 40% at advanced adoption levels. Advanced adoption means production workflows, not pilots. Production workflows require data that is accurate, auditable, and traceable. That is Daloopa's entire product proposition. Brighton Park's special advisor for this round was Phil Hadley, former CEO and Chairman of FactSet, one of the largest financial data companies in the world (valued at approximately $17 billion). FactSet and Bloomberg Terminal together have dominated financial data infrastructure for decades. Hadley's involvement signals that Brighton Park understands financial data at the incumbent level and believes Daloopa is building something that matters in the next era. When the former CEO of your most relevant incumbent competitor advises the lead investor, the due diligence is unusually rigorous and the conviction is correspondingly high. Squarepoint Capital's participation is the user validation signal. Squarepoint is a quantitative hedge fund that manages billions in capital through algorithmic strategies. They do not make venture bets for financial returns at their scale. They invested because Daloopa's data is already in their research infrastructure and they want to ensure it remains well-resourced and competitive.
Open roles at Daloopa
5 positions we're tracking. Roles are re-checked daily and removed when filled.
Product Manager
First seen 2 months ago
Product Operations & Analytics Lead
First seen 2 months ago
Customer Success Manager
First seen 2 months ago
Account Executive
First seen 2 months ago
Business Development Representative
First seen 2 months ago
Know when Daloopa is hiring before anyone else
A role stays uncontested for about four days. Here's the window — and where we put you in it.
From $9/month, cancel any time.
Watching Daloopa
0 applicantsRole spotted & verified
1You get the alert
1You've applied
~8Hits the job boards
250+Hiring outlook
Very High. The press release explicitly names team expansion across engineering, product, and go-to-market as a capital use. Revenue doubled year-on-year. Five confirmed live roles on Built In and LinkedIn at the time of writing.
Working at Daloopa
Since 2019, Daloopa has built Source-linked financial data infrastructure for AI and agentic investment workflows. The team is now 50-200 people. Working at a AI company at this stage means defined function but no bureaucracy, you'll own a surface area rather than a ticket queue.
Most openings are based out of New York.
How to actually get hired at Daloopa
Why applying the normal way doesn't work
There's an ATS, but at Daloopa, referrals get priority. Your cold application competes with sourced candidates and internal recommendations.
Who to contact at Daloopa
What to show them
Thomas Li named "Scout" as a specific product Daloopa is investing heavily in: a tool to help customers automate parts of research, modeling, and analysis workflows on top of the data layer. The PM who owns this roadmap sits at the intersection of financial workflows and AI agent behaviour. The job spec on Built In asks for someone who can oversee the roadmap for AI-powered financial products, enhance user experiences, and conduct market research. At a company where the customers are hedge fund analysts and quant researchers, market research means understanding workflows that are genuinely complex. Core skills: Product management for B2B SaaS or financial data products, financial services domain knowledge (buy-side investment research preferred), AI/agentic product experience, roadmap prioritisation, user research with technical domain experts, ability to translate analyst workflow nuance into product requirements Proof of work: Map a specific workflow in equity investment research (earnings model update, sector comparable analysis, or portfolio screening) and write a one-page product brief describing how Daloopa's data layer enables an AI agent to automate it. Include: the data inputs required, the failure modes that occur when data is web-scraped versus source-linked, and what the PM would need to learn to validate whether Scout's current design addresses those failure modes. Specific data point examples (EPS, EBITDA margin with fiscal calendar normalisation issues, etc.) are the proof of domain knowledge.
A cold email that works at Daloopa
What Daloopa screens for
The Series C announcement explicitly states the capital will expand the team across engineering, product, and go-to-market. Revenue doubled year-on-year. The milestones Thomas Li named for the next six months in the AlleyWatch interview are specific: deeper dataset coverage, more AI and agent workflow products (Scout specifically named), and expanded integrations. Each translates directly to a hiring profile. Built In currently lists five roles (daloopa.com/careers and builtin.com/company/daloopa/jobs). Email hello@daloopa.com reaches the team directly for roles not yet listed.
Customize your CV for the Daloopa role. Matching the job description language helps clear ATS filters.
Don't make these mistakes
Generic enthusiasm about 'AI in finance' or 'the future of investment research.' This team has been solving this problem since 2019, doubled revenue year-on-year, and just closed a $47M Series C. They have heard every version of the pitch. Lead with what you know about their specific product, their specific customers (hedge funds, mutual funds, bulge-bracket research teams), and the specific workflows their platform changes. Show the work before you show the interest.
Mistakes that kill Daloopa applications
Generic CVs stand out at a 50-200-person company — and not in a good way. Fastest path to rejection.
Don't open with what you want. Open with what Daloopa is dealing with right now — Investment research is one of the largest and most data-intensive white-collar workflows in the world, and what you'd do about it.
The wait-and-hope strategy fails. Follow up on day five — response rates roughly double.
Applying to Daloopa? Get the contact, not the form.
The Daloopa interview process
4 stages · 14 days typical · take-home: yes · modelled from similar companies
We don't yet have verified candidate reports for Daloopa. What follows is the typical process for a 50-200-person AI company — treat it as a model, not confirmed detail.
Interview stages
Recruiter Screen
Phone or video · 30 min
Basic qualification and logistics
Recruiter or HR
Hiring Manager Interview
Video call · 45 min
Role fit and experience deep-dive
Hiring manager
Technical/Functional Round
Video call · 60 min
Skills assessment and problem-solving
Team members
Final Round
In-person or video · 60 min
Culture fit and cross-functional alignment
Senior leadership
Daloopa take-home assignment
Daloopa includes a take-home exercise in their interview process. For AI roles, this typically involves a practical problem that takes 2-4 hours. Focus on clean, working code over premature optimization. They're evaluating how you think and communicate, not just the solution.
Daloopa interview timeline
Expect days total. Compared to similar AI companies (18 days median), Daloopa is faster.
Interviewed at Daloopa?
Tell us how it went — stages, questions, timeline. Takes 90 seconds and it's how this page stays accurate for the next person.
Submit your Daloopa interview experience →Daloopa jobs, frequently asked questions
How many jobs does Daloopa have open?
As of June 2026, Daloopa has 5 open positions.
Does Daloopa hire remotely?
Daloopa doesn't have remote openings at the moment. All roles are in New York.
What roles is Daloopa hiring for?
Daloopa is hiring across Product, Operations, Customer, Sales. The most recent opening is Product Manager.
How do I apply for a job at Daloopa?
Use the apply links above, or check our guide to getting hired at Daloopa.
Does Daloopa respond to cold emails?
Not enough data yet on Daloopa's cold email response rates.
Who is the hiring manager at Daloopa?
At this size, hiring is usually run by the hiring manager or department lead.
How competitive is it to get hired at Daloopa?
Expect 150-300 applicants in the first two weeks for AI roles at this size. Apply within 72 hours for best odds.
How many rounds is the Daloopa interview?
4 stages: Recruiter Screen, Hiring Manager Interview, Technical/Functional Round, Final Round.
Is the Daloopa interview hard?
Expect technical depth and system design, not algorithm trivia. Candidates report Technical Interview as the toughest stage.
Does Daloopa give a take-home task?
Yes, Daloopa includes a take-home assignment.
How long does Daloopa take to get back to you?
Around 14 days across the full process.
What should I prepare for the Daloopa interview?
Focus on technical depth and system design. At 50-200 people, they're testing whether you can operate without process, not whether you memorised algorithms.
Where is Daloopa based?
Daloopa is headquartered in New York, US.
Get Daloopa roles before they're posted
A role stays uncontested for about four days. Here's the window — and where we put you in it.
From $9/month, cancel any time.
Watching Daloopa
0 applicantsRole spotted & verified
1You get the alert
1You've applied
~8Hits the job boards
250+