Engineering Manager, Scout

Daloopa is transforming how investment professionals work. Our mission is to eliminate the slow, error-prone parts of fundamental research, without sacrificing accuracy or auditability.
Founded by a former equity research analyst and top engineers, we built an AI-powered platform that converts complex financial filings, transcripts, and KPIs into clean, hyperlinked, customizable data. This means analysts spend less time on manual tasks and more time generating insights that drive performance.
Today, Daloopa powers the workflows of leading investment teams worldwide by helping them move faster, think deeper, and make every decision with confidence. If you’re passionate about building technology that solves real-world problems and want to make an impact, join us and help redefine the future of investment research.
Competitive pay + performance incentives
Equity in a fast-growing FinTech
Hybrid work schedule (3 days in-office for HQ based roles)
Career growth and mentorship opportunities
A vibrant, collaborative culture
Full benefits package
About the roleLocation:
Remote;
Canada
Compensation:
$230,000CAD - $245,000 CAD base + equity + benefits (This range is reflective of level of contribution. Candidates who fully match what we’re looking for in this role will be at the top of the range.)
About the Role:Every number an investment professional relies on starts in a filing, a press release, or a transcript, and getting it into a model has always meant hours of manual extraction with no easy way to check where it came from. Daloopa solves that. We automate the extraction, organization, and delivery of deeply sourced, audit-ready financial data, with every figure traceable to its source, so clients update models faster, make fewer errors, and spend their time on analysis. Through MCP for Financial Services, the same verified data flows directly into AI agents and industry workflows. Daloopa is trusted by leading global institutions such as Morgan Stanley and backed by prominent investors.
A financial model has always been bounded by what one analyst can maintain by hand. Coverage, depth, and the questions worth asking all stop at the same line, and the ambitious ones never get asked because nobody can afford the answer. Scout moves that line. It runs inside Excel on Daloopa's audit-ready data, and builds and updates models with every reported figure hyperlinked to its source, which is why it's more accurate than other AI Excel agents.
When work that good is near-free, the questions get bigger. Instead of "did I get this quarter's numbers into my model", analysts start exploring "how does this KPI behave across every player in the sector over ten years, and what does that say about the one I own?" and "What am I seeing in this sector that others haven't connected the dots on yet?"
We're hiring an Engineering Manager to lead the team building that.
Who You Are
You ask instead of assuming. If something is unclear, you ask rather than filling the gap with a guess and building a plan on top of it. You build a team that does the same
You think in business value. You can explain in one sentence how any piece of your team's work helps the customer or the business. If you can't, you treat that as a signal it isn't ready
You protect scope and default to simplicity. You insist on clear, testable outcomes before work starts, push for the simplest version that delivers the value, and raise concerns before the sprint begins, not in the retro
You own outcomes end to end. Hand you an ambiguous problem and you drive it to completion, including the work that lands on your team without an owner. Nobody has to chase you, and nobody has to chase your team
You communicate proactively. You surface risks, issues, and updates early and often, before anyone has to ask. No surprises for your team, your stakeholders, or your clients
You're driven from the inside. Nobody needs to set the bar for you. You set it higher than anyone around you would
You're self-aware and learn fast. You can talk plainly about your weaknesses and what your mistakes taught you, you handle disagreement well, and you pick up new tools and domains quickly
You raise the bar around you. You share what you know, grow the people you manage, and make the team better rather than making yourself the bottleneck.
Success Looks Like
You're both of these. This isn't a pure people-management role
The player coach. You never stopped building. Give you two days and an AI coding tool and you'll come back with a working Scout prototype, then teach the team how you did it
The manager people follow. When you moved on, people came with you, or they'd tell us they grew more under you than anywhere else. You can name them
A track record you can put numbers on. You can name two or three things you or your team made measurably better and say what the number went from and to. Your own ideas and their business impact, not a list of responsibilities
You build strong teams. A history of hiring, developing, and retaining strong engineers, and of building healthy teams that run on trust, accountability, and continuous improvement
Hands‑on technical leadership (e.G., backend, full‑stack, or AI products). You engage deeply in design and code reviews, and you set and uphold high standards for execution, code quality, and operational readiness in client‑facing production systems
Experience shipping client‑facing products built on ML/LLMs, and the engineering practices required to run them reliably in production: evals, observability, latency, and cost
You own the roadmap with Product. You define and manage it with Product and cross‑functional stakeholders, making clear tradeoffs between scope, quality, and timelines
You improve how teams work beyond your own, through initiatives such as incident reviews, standards, tooling, or knowledge sharing
You build trust across the company and with clients. You have strong working relationships across Engineering, Product, and client‑facing teams (Sales, Customer Success, Support), and you're comfortable in front of the analysts who use what your team ships.
Preferred
Experience with financial data products or pipelines (e.G., fundamental data, market data, research or analytics platforms)
Experience building products that live inside tools users already work in, such as Excel or Office add-ins
You've been exceptional at something outside your job: a top ranking in a game or sport, a competition win, or side projects with real traction
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