Data engineer

We are hiring our first Data Engineer to own the database our agents and outreach are built on.

Paires is where founders come to raise capital. We pair them with the right investors from a large, engaged global investor network, then run the warm outreach that turns into meetings. It is a two-sided platform, live with paying clients, profitable and self-funded, built by a small, senior, flat team that ships fast.

The role

Everything we do runs on one asset: a database of every company and investor out there, every funding round, the news that matters, and how they all connect - plus the raw context underneath: every email and call transcript, linked to the right people and companies. It is a knowledge graph and a memory in one. Our matching, our outreach, and our agents are built on top of it, and it grows faster than anyone can own it on the side. You become its owner. You design it, scale it, keep it clean, and turn it into the single source of truth that everything reads from. To be clear about the shape of this seat: it is not a reporting or analytics warehouse. It is the memory a live product thinks with, built for one reader above all: agents retrieving exactly the right fact at the right moment. One honest filter before you apply: if the database you are proudest of tracked shipments, sensors, factory lines, or compliance - however well you built it - that is a different seat. If it tracked companies, investors, deals, and the people and conversations around them, keep reading.

What you will own

• The database itself: Postgres and Supabase with hybrid search, schema design, modeling, scaling, and performance as it grows without a ceiling.
• The agents that read it run on Pydantic AI and the Claude Agent SDK, on AWS. We are consolidating into pgvector, not buying a vector DB.
• Data quality end to end: validation gates for vendor and third-party data, dedup, entity resolution, provenance, monitoring.
• The communications layer: raw emails and call transcripts stored, linked to the right people and companies, and searchable.
• Ingestion and enrichment pipelines: funding rounds, market news, and contact and company research at scale, engineered for cost and freshness.
• The knowledge graph: companies, investors, funding rounds, and news as entities and relationships - node and edge tables in Postgres, provenance on every fact.
• The unified data layer: one clean spine that every campaign, agent, and product feature reads from.

You are a fit if you

• Have owned a database of companies, people, deals, or the communications between them - a CRM source of truth, a market or deal intelligence graph, an enrichment layer - that a live product, agents, or a sales team read from. Serving dashboards is a different job than this one.
• Are strong in SQL and Python, with real pipeline work behind you: ingest, transform, dedup, enrich.
• Have caught bad data before it hurt the business, and can tell us how.
• Think in schemas and contracts, and design for the queries of a year from now.
• Have modeled entities and relationships at scale - companies to investors to rounds to people - and kept the connections queryable as the sources multiplied.
• Move fast with AI tooling and own outcomes.
• You do not need the title. If you were the RevOps or growth person who owned the CRM data, the enrichment pipelines, and the dedup nobody else wanted - and you got real hands-on with AI - we want to hear from you.

Bonus

pgvector and embeddings, a knowledge graph you modeled in a relational database, funding-round or news ingestion at scale, entity resolution at scale, a raw communications store you built yourself.

What we offer

Fully remote and async. Your day overlaps with US Eastern time for a few hours - not full US hours. Meetings batch on Mondays and Thursdays, the rest is deep work. The best AI tooling, paid (Claude Code, Cursor, top models). You work alongside our GTM lead and our founding engineers, and your layer feeds everything they build.

How to apply

hit apply, which takes you to our short application form. We read every application.