Staff Data Engineer

About Agora Intelligence (d.b.a. Tilt) Agora Intelligence is building the audited methodology layer agents call before touching portfolios. Our product, Tilt , 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. Tilt 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 Tilt , the reference implementation of InvestOS. Tilt is an agentic harness where investors, portfolio managers, and creators can author, remix, and publish strategies. Were 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.

What Were Building 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 Tilt, anyone can express a view as a rules-based basket of assets in seconds, backed by evidence and scored against what actually happens.

Whether youre 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 tilt : a creator-authored view of the world.

The bigger bet: as AI agents take on more of investing, theyll need an open, trusted standard for how investment views are expressed, verified, and combined. When anyone can generate a strategy in seconds, the strategies arent the asset. The system that versions, tests, and grades them is. Were 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.

Who We Are

Were 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. The Opportunity Creating an investable index from a conversation with AI isnt 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. Were looking for a Staff 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. Youll work directly with the founders, product engineers, and AI/ML researchers to build the data foundation that powers everything we do. This isnt a role where youll simply maintain pipelines. Youll help identify opportunities, influence our data strategy, and set the standard for data engineering across Tilt.

What Youll Do

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.

Who You Are

Youve 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 its stored. You have strong engineering fundamentals and are comfortable designing distributed data systems a