Founding Data Engineer

pEvery company wants its own AI. Almost none of them have their data ready for it. /p /brpAsk any organization where its knowledge lives and the answer is: everywhere. SharePoint, databases, PDFs, ticketing systems, email archives, a data warehouse in the cloud and a server in the basement. That data is exactly what makes a company's AI valuable, and exactly what makes it hard to build. /p /brpThat's what we're solving at R3AL.
AI. /p /brpWe help organizations build their own AI: open models, fine-tuned on their own data, optimized and running on infrastructure they control. No sensitive data sent to third parties, no vendor lock-in. We work with insurers, government and healthcare organizations, where data privacy isn't optional. /p /brpOur platform runs the full cycle: data → fine-tune → evaluate → deploy, and then again whenever data grows or a better model comes out. The first step of that cycle is the one everything else depends on. That's where you come in. /p /brpWe're early. Very early. /p /brpThis is not a \"join a big team and own a narrow box\" kind of role. You'll be building the data layer of the platform from the ground up, together with the founding team. /p /brh3What you'll do /h3 /brpAs our Founding Data Engineer, you'll own everything between a client's raw data and a model that's ready to learn from it. /p /brpYou'll: /p /brul /brlibuild connectors that let clients plug our platform into their data sources easily, whatever the format, tool or

location:
local, on-prem or cloud /li /brlidesign automated pipelines that ingest, clean, deduplicate and structure data, from databases to messy unstructured documents /li /brliturn raw company data into ML-ready assets: fine-tuning datasets, evaluation sets and retrieval-ready knowledge /li /brlibuild in data privacy from the start: PII detection, anonymization, access control and data lineage /li /brlimake pipelines repeatable and versioned, so every new data drop or model release can flow through the cycle automatically /li /brliwork closely with the founders on the product roadmap, technical direction and how we onboard clients /li /br /ul /brh3Who you are /h3 /brpYou probably have: /p /brul /brlisolid experience as a data engineer or data scientist building production pipelines /li /brlistrong Python and SQL, plus experience with data processing tools (Pandas, Polars, Spark or similar) /li /brliexperience connecting many types of sources: relational databases, data warehouses (Snowflake, BigQuery, Databricks), object storage, APIs and document stores /li /brlifamiliarity with orchestration and transformation tools like Airflow, Dagster, Prefect or dbt /li /brlicomfort working with Docker and deploying on both cloud and on-prem environments /li /brlibonus points if you've prepared data for LLM fine-tuning, RAG or evaluation, processed unstructured documents (PDF, Word, email) at scale, or worked in regulated sectors with GDPR-sensitive data /li /br /ul /brpBut beyond the technical side, we're looking for someone who: /p /brul /brlilikes ownership and has an entrepreneurial mindset /li /brlithinks like a builder, not just an executor /li /brlienjoys messy problems and turning them into clean systems /li /brliwants to be early, not comfortable /li /brlicares about building something that could genuinely matter /li /br /ul /brpBecause this is the kind of role that gives you far more than just a title. /p /brpYou'll get real ownership from day one. Not just over pipelines, but over how the product works, what clients experience when they connect their data, and how we build the company. /p /brpYou'll work directly with the founders, see your work go live at real clients, and get exposure to far more than engineering alone: client onboarding, go-to-market, strategy and the full process of building a company from the ground up. /p /brpAnd yes, this role comes with a strong compensation package that reflects the level of ownership, ambition and impact expected from an early hire. /p /brpYou won't be joining as employee number 47 to maintain one tiny corner of a mature data stack. You'll be one of the first people shaping what R3AL.
AI becomes, technically, strategically and culturally. /p /brpIf you want a predictable corporate data role, this is probably not it. /p /brpIf you want to build the foundation that lets every company own its AI, we should talk. /p