Senior Technical Product Manager — Document Intelligence

Senior Technical Product Manager — Document Intelligence LocationAmsterdamEmployment TypeFull timeLocation TypeHybridDepartmentProductAbout the roleWe're looking for a Technical PM to own the document intelligence platform — the tooling that turns messy, unstructured documents into clean, structured, agent-ready data. Extraction, classification, summarization, storage, indexing, and retrieval of document-based data are the raw material every agentic workflow at DataSnipper depends on.This is a technical, engineering-facing role focused on tool and data infrastructure, not agent orchestration. You’ll own the critical extraction and processing layers that drive the core of our agentic work. If the extraction is wrong, every agent downstream inherits the error; your job is to make the data foundation accurate, fast, and reliable at scale.It's a product role at heart: you translate what customers and the agent teams need into the architecture and quality bar that delivers it — product judgment expressed through technical decisions. You'll spend your time in pipeline and data-architecture discussions alongside ML and backend engineers, defining extraction quality and the evals that measure it. When you work with go-to-market, it's to turn accuracy, coverage, and reliability into a story the market trusts.If you're passionate about turning messy, unstructured documents into clean, reliable data — and you've dug deep on something like extraction, classification, parsing/OCR, or measuring data quality at scale — this role is for you.About DataSnipper Audit and finance are still massively manual and we are changing that. DataSnipper is a $1B, bootstrapped unicorn with 600,000+ users across 180+ countries, already embedded in the daily workflows of top audit and accounting firms.Now, we are taking things further with our Excel Agent, bringing AI directly into where the work actually happens. Unlike generic AI tools, we do not sit on the sidelines. Our AI operates inside Excel, with access to real documents and audit evidence, meaning it does not just generate answers, it does the work, with full traceability.We are not just applying AI, we are redefining how audit gets done. If you want to build something category-defining at scale, this is the place.What you'll ownThe document intelligence pipeline. Technical direction for how we ingest, parse, extract, classify, and summarize unstructured documents — the tooling that converts raw documents into structured, queryable data. Accuracy and coverage across document types are your core mandate.Product strategy. Own the product strategy for the document intelligence platform, translating customer and agent-team needs into clear requirements and the tooling roadmap (APIs, workflows, and internal platforms) that makes those needs shippable and maintainable.Document storage, indexing & retrieval. How processed documents and their derived data are stored, indexed, and served — so agentic flows can retrieve the right information quickly and reliably.Non-functional requirements of the platform. How well the platform runs — extraction accuracy, throughput, latency, cost per document, reliability, scalability across document volume and variety, and observability. These are your primary success metrics, not feature counts.Quality & evals. Own the creation and running of the evals that measure and improve extraction, classification, and summarization quality at scale — defining the quality bar each capability meets before it ships. You work hand in hand with our LLM Ops team, who own the eval infrastructure; you own the evals themselves and what they tell us.Model & tooling strategy. Decisions on models, extraction techniques, and build-vs-buy across the document-processing stack; cost/performance/accuracy trade-offs; staying current as document-AI techniques evolve.Cross-functional partnership. Engineering is your primary partner — you operate as a technical peer to engineering managers and tech...