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DatasnipperVerified employer
Senior Technical Product Manager — Document Intelligence
Amsterdam, Netherlands
HybridFull-timeManagerProduct
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Amsterdam, Netherlands
You apply directly with the employer. We never charge applicants.
Utrecht, Netherlands
Pay not published. Too few similar jobs show pay for a fair estimate.
We'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.
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.
The 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 leads. You serve the agent teams (your data is their foundation) and partner closely with the Integrations team on how documents get in and results get out.
A tinkerer. You build to understand — you'll prototype an extraction or test a classification approach rather than theorize about it.
A product thinker in an engineer's seat. You don't need to be an auditor, but you love representing the customer and the business problem — and turning that into a robust, well-architected data platform. The technical depth is in service of product value, not an end in itself.
A systems-and-data mind. You think in pipelines, data quality, and infrastructure that holds up under volume and variety.
Highly autonomous and entrepreneurial. You find the problems that matter, set direction, and drive without waiting to be told. You treat your area like your own company — scrappy, outcome-obsessed, comfortable under ambiguity.
Must-have:
4+ years in product management, with 2+ years on technical/platform/data products (APIs, infrastructure, data pipelines, ML systems, or developer tools)
Strong grounding in document or data processing: extraction, classification, parsing/OCR, summarization, or other intelligent-document-processing / NLP techniques — with enough grasp of LLM-based approaches to judge when they're the right tool
Background in distributed systems, data infrastructure, or pipelines — you understand how processing systems behave under scale, volume, and variety
Treats non-functional requirements as a first-class product surface: accuracy, throughput, latency, cost, reliability, observability
Hands-on experience creating and running evals or quality-measurement methods for AI/ML or data systems at scale
Fluency in model and tooling operations: model/technique selection, build-vs-buy, cost/performance/accuracy trade-offs
Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (accuracy vs. cost, coverage vs. latency)
Strong-to-have:
Former software engineer, ML engineer, or data scientist who moved into product
Hands-on experience with document AI / IDP, OCR, NLP, or unstructured-data systems specifically
A public point of view on AI or document intelligence — writing, talks, OSS — or the appetite to build one
Experience in audit, accounting, or financial services (domain context for the documents we process)
Be part of one of the fastest-growing, profitable unicorn scale-ups in the Netherlands with a global impact.
Equity (Stock Appreciation Rights) to share in the company’s success and growth.
Pension plan with a 6% contribution on top of your base salary.
28 vacation days per year (full-time) to support your work-life balance.
Hybrid work model with at least 3 days onsite in our dynamic Amsterdam office.
Daily, freshly prepared lunches by our in-house chef to keep you energised.
NS business card for easy commuting to the office.
A structured onboarding programme designed to set you up for success, including dedicated time to learn our product and customers before you hit the ground running.
Access to continuous learning and development initiatives to grow your skills.
Engage with a vibrant international team spread across seven global offices.
Company-wide events like DataSnipper GO, where global teams come together.
Access to OpenUp, a mental health and wellness platform supporting your wellbeing.
Recruiter screen
Hiring Manager interview
Assessment interview
Final interview with Engineering leadership