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Data at Specter 👻

At Specter, data is what it's all about. We're here to give our clients, some of the most legendary funds in the world, the most actionable intelligence to drive investments into great companies and people.

We’re proud to move fast at Specter. Check out our release notes to see what’s new on the product side—but there’s just as much happening on the data side, both architecturally (moving to click-house, pandas to polars), as well as on improving our data and introducing new features.

AI has unlocked a ton of exciting opportunities. We’ve built some of the highest quality data in the industry, and that gives us an incredible springboard to take advantage of all the possibilities today, as we’ve already been doing on the product side with our recent AI Search and Similar Companies, and a lot more to come.

The Role

We're looking for a senior data engineer who cares deeply about data quality and getting to the best possible result. The role sits at the intersection of data engineering, backend, and AI — and the balance between those shifts depending on what we're building.

Specter is a deliberately small and senior team, which means everyone has T-shaped specialities and does best when they're not afraid to reach into different areas. We're also at a point of rapid growth, and we're looking for someone to help us co-architect the data stack and bring it to the next level.

Some weeks you'll be designing and optimizing data pipelines across our 30+ sources. Other weeks you'll be writing a multi-stage LLM pipeline or building event-driven analytical systems. What ties it all together is caring about the data itself — understanding what makes it good, what's wrong with it, and how to make it as useful as possible for our clients.

What you'll do

• Build and maintain scalable data pipelines. Design and optimize ELT pipelines that process large volumes of data reliably. Work with OLAP and OLTP databases, ensure data quality, and follow strong modeling practices.
• Build AI-powered data systems. Write multi-stage LLM pipelines, work with embeddings and structured outputs, and help define the patterns we use to integrate AI into our data and product.
• Own projects end-to-end. A recent example: our "duplicates" project involved identifying duplicate companies across the platform, refactoring how we represent entity relationships (e.g. parent/subsidiary), building the resolution logic, and setting up monitoring — all as one cohesive effort.
• Raise the bar on architecture and quality. Contribute to good architectural decisions across the data stack. Help establish standards for documentation, testing, and system design that make the whole team better.
• Work closely with the business. Collaborate with product, founders, and clients to define what a project actually means before building it — and iterate on it once it ships.

Who you are

You get genuinely excited about data and take real ownership of what you build. You care about the end result, not just the technical exercise.

You bring: