Principal Data Engineer

1 мес. назад

210k–270k USD / yearUSALeadRemote

pythonsqlawssparkkafkaetldata-warehousing

Principal Data Engineer role focused on building an open platform and marketplace for rules-based investment methodologies integrating AI and human authored strategies.

О компании

We're a team of ~25 senior-to-principal engineers, designers and AI/ML researchers. More than half the team are former founders. We move fast, challenge assumptions, and believe AI should fundamentally change how financial products are built. We have no management hierarchy and work remotely with 4 one-week hackathons per year. “Full stack” to us means you own a slice of the product, including its roadmap and go-to-market.

Обязанности

Creating an investable index from a conversation with AI isn't a traditional data engineering problem. Every investment methodology we generate depends on high-quality, continuously evolving data flowing reliably across hundreds of sources, pipelines, and systems.
We're looking for a Principal Data Engineer who thrives in ambiguity, enjoys building large-scale data platforms, and wants to define how AI-native investing is powered behind the scenes.
You'll work directly with the founders, product engineers, and AI/ML researchers to build the data foundation that powers everything we do. This isn't a role where you'll simply maintain pipelines. You'll help identify opportunities, influence our data strategy, and set the standard for data engineering across .
Design and build the data platform that powers methodology generation, AI reasoning, analytics, and investment products.
Develop reliable, scalable pipelines that ingest, transform, and enrich structured and unstructured financial information.
Build systems that continuously process data from filings, market data, news, research, and other external sources.
Design data models that support product development, machine learning, and long-term platform evolution.
Ensure our data is accurate, observable, reliable, and available when every downstream system depends on it.
Make thoughtful tradeoffs between data quality, performance, scalability, and operational simplicity.
Raise the quality of data engineering across the company through technical leadership, architecture, and best practices.
Continuously improve how we build by leveraging AI throughout development, testing, monitoring, and data quality workflows.

Требования

You've spent years building production data platforms, not just ETL pipelines, and understand the difference.
You naturally think about how data enables products, not just how it's stored.
You have strong engineering fundamentals and are comfortable designing distributed data systems at scale.
You're able to simplify complex data architectures into systems that are reliable, maintainable, and easy to extend.
You understand that trustworthy AI starts with trustworthy data.
You communicate technical ideas clearly and enjoy working closely with engineers, AI researchers, and founders.
You're comfortable taking ownership without waiting for direction and enjoy solving foundational infrastructure problems.
You believe AI should fundamentally change how financial data is collected, processed, and used.

Условия

Competitive base salary and meaningful equity package
Direct partnership with the founders and a team where more than half are former founders.
Comprehensive health and dental benefits.
A remote-first, flexible work environment.
The opportunity to help define a company creating a new category in capital markets.

Другое

Agora Intelligence is building the audited methodology layer agents call before touching portfolios. Our product, , is GitHub for rules-based investment methodologies: an open platform and marketplace where investment strategies are created, tested, audited, and distributed — by humans and AI agents alike. As AI agents begin managing real portfolios, they need a trusted source of verified investment logic before they can act. is that source.
We operate across two connected surfaces: InvestOS , an open standard that lets any brokerage or platform plug verified methodology into its own AI experience; and , the reference implementation of InvestOS. is an agentic harness where investors, portfolio managers, and creators can author, remix, and publish strategies. We're a small, senior team (from BlackRock, Bloomberg, BGI, and Quant Funds) backed by Portage Ventures, Lerer Hippeau, Cumberland Investments (and more), working with major brokerages and financial data providers.
InvestOS — an open standard for rules-based methodologies and its reference implementation.
We believe any idea — a theme, a trend, a thesis — can be turned into an investable view. On , anyone can express a view as a rules-based basket of assets in seconds, backed by evidence and scored against what actually happens. Whether you're modeling a thematic strategy, tracking an emerging technology, or capturing the public-market footprint of a private company, what used to take an index committee months now takes an API call — 100× faster, at a fraction of the cost. We also transform views found on the open web into rules-based strategies. This lets us measure the performance of any opinion or prediction about the future. The unit is a : a creator-authored view of the world.
The bigger bet: as AI agents take on more of investing, they'll need an open, trusted standard for how investment views are expressed, verified, and combined. When anyone can generate a strategy in seconds, the strategies aren't the asset. The system that versions, tests, and grades them is. We're building that standard — and the platform where those views are made. We’re also building the learning loops for when views resolve against reality, as measured by markets.
Experience building large-scale data platforms, streaming pipelines, or modern data infrastructure.
Experience working with financial, market, or other high-volume information sources.
Strong experience with distributed systems, cloud infrastructure, and data orchestration.
Experience supporting machine learning or AI platforms with high-quality data pipelines.
Experience at an early-stage startup where you've owned significant platform or infrastructure decisions.
You're energized by building systems that quietly power everything else.
A link to your GitHub, portfolio, or examples of your work.
A short note about a data platform or infrastructure project you've built that you're most proud of, including the problem you solved and your role in the outcome.
A few sentences on why you're interested in .