Data Infrastructure Engineer, AI Agents Filled Own the data wrangling function — ingestion, cle[...]
Arctal · London, United Kingdom ·
- Employment
- Full time
- Category
- Devops
- Experience
- 3+ years
Arctal · London, United Kingdom ·
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blockit · San Francisco
Full-time, in-office — Old Street, London.
Competitive + meaningful equity · 3–5 years experience
Arctal builds structured datasets from unstructured financial documents—100,000+ PDFs (fund reports, regulatory filings, investor letters) turned into clean, queryable data that institutional buyers use for decision-making.
AI agents do the reading. We build the agents. Team of 5, output of 50.
A dataset is not a fact. It's a representation of reality that someone chose to stand behind. The value is in that judgement—in making the representation trustworthy and maintaining that promise over time. AI agents do the extraction and structuring. They cannot be the ones standing behind it.
Our customers are asset managers, allocators, and financial data firms who need reliable data extracted from documents that were never meant to be machine-readable.
You'll own data wrangling end-to-end. Ingestion, cleanup, storage, transformation, distribution.
At Arctal, every person is building themselves out of their current role—automating the task they did yesterday so they can take on the harder problem tomorrow. This isn't a feature of being early-stage. It's the model.
Data Pipeline Architecture. Build the infrastructure that turns 100,000+ unstructured PDFs into constantly updating datasets. Own architecture decisions, pipeline reliability, data quality, delivery format. Design systems that scale with AI agents doing the heavy lifting.
AI Agent Integration. Work with existing AI agents and build new ones. Push the boundaries of what's possible with agentic tooling. Figure out how to get agents to do the work reliably, at scale.
Data Quality & Delivery. Care deeply about data quality—the datasets we build inform decisions that move millions of dollars. A missing data point isn't trivial. An anomaly isn't something to gloss over. Ship great data as a product.
If something breaks at 2am, it's yours. If something ships to a client faster than anyone expected, also yours.
The builder-operator. You don't see a line between building the system and understanding the business. You can architect a pipeline and explain to a client why the methodology matters. 3–5 years building data pipelines or ML systems. You've shipped in a small team (2–30 people) and know what it means when there's no one to hand things off to.
The AI-native engineer. You use Claude Code or Cursor daily as your base infrastructure. You've been deep in the agentic tooling ecosystem—building, testing what breaks, pushing boundaries. Your first instinct facing a new problem is to design a system that solves it autonomously.
The data quality obsessive. You care about data quality in a "this inconsistency is going to bother me until I fix it" way. You're persistent, thorough, and you've done this before.
What matters: Python, Postgres, SQLite, async, queues, web scraping, LLMs in production. You think end-to-end, from raw input to reliable output. You care about the outcome for the user, not the elegance of the system. You don't wait for tickets—you drive your own projects.
You experience genuine satisfaction in automating yourself out of work, because it means you get to take on the harder problem behind it.
Aleksi (CEO) — Cambridge engineering + ML. Co-founded Secondmind, founding team at Sylvera. Previously worked on ML with Carl Rasmussen at Cambridge.
Krista (CCO) — Former Head of Market Intelligence at Climate Bonds Initiative. Deep sustainable finance and capital markets expertise.
Small team that ships fast. No layers, no politics—just building.
CTO trajectory for the right person. Or the fastest education in data infrastructure and AI agents you'll find.
kaizenlabs · Washington, D.C.