> Markdown version of [/jobs/ext/2722496-founding-data-engineer](https://www.wearedevelopers.com/jobs/ext/2722496-founding-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Data Engineer - **Company:** Perceptive missions LLC - **Location:** New York, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Information Engineering, Machine Learning, Operational Databases, Large Language Models, Data Management, Databricks - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/founding-data-engineer-percepta-8906177 ## About the Role You might come from any point on the spectrum - a strong data engineer; a software engineer who's done real data work; someone who's done data science and software; or an ML engineer who now wants to build more. What's common: you can build in ambiguity, you form opinions and ship, and you care about building leverage, not just outputs. * Strong experience around some combination of Data Science, Data Engineering, Machine Learning. * A product instinct for the second half of the job - you want to build the thing that makes the work easier, not just do the work * Intuition for what modern AI/ML and LLM systems actually need from data (features, retrieval, context, embeddings) * High ownership and strong communication - you're comfortable embedded directly with customer teams Nice To Have * Experience building agentic or automated data-engineering tooling * Hands-on experience with modern cloud data platforms (e.g., Databricks) * Experience with health-system data (EHR, claims, and other operational healthcare datasets) or other complex, regulated enterprise data * Prior startup, founding, or forward-deployed experience ## Description * Build end-to-end pipelines and models that turn fragmented, messy enterprise data into high-leverage, AI-ready assets * Structure and normalize noisy datasets - defining the data packs and ontology that our AI engineers build on top of * Build the internal product and tooling that makes data work faster and repeatable across customers, so each engagement compounds rather than starts from zero * Work directly with operators and product/AI engineers to turn high-value use cases into production data workflows * Form strong technical opinions on data models, storage, orchestration, and infra tradeoffs - and make the calls, Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will. Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn't a 9-5, nor is it a job we're ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care. Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do-the products we build, the partnerships we launch, the strategy we set-exists to make our customers successful. Delivery is the strategy. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [It's all about the Data](https://www.wearedevelopers.com/videos/425-it-s-all-about-the-data) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)