Senior Technical Product Manager — Document Intelligence

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.

ABOUT DATASNIPPER

The hardest AI problems aren't about getting an answer fast. They're about getting one people can trust.

We believe trust is essential to how society works. Every day, we rely on information that's been independently verified, from financial statements to the products we buy, to the sustainability claims companies make. Behind that trust are audit and finance professionals who examine evidence, verify information and hold organizations accountable.

At DataSnipper, we build AI solutions that empower those professionals. By making evidence easier to trace and verify, we help them focus on the judgment and expertise that make their work so important.

For our product and engineering team, this is where things get exciting. In audit, a plausible answer isn't good enough. Every output has to be explainable and traceable back to its source. We've just launched a brand new agentic platform, Alwin by DataSnipper. AI agents running in the cloud across hundreds of documents at once, reasoning across PDF, Word and Excel, with a full audit trail behind every step. There are lots of greenfield problems ahead, and real room to shape what we build and how we build it.

And we build at scale. More than 600,000 professionals across 3,000+ organizations in 175 countries rely on DataSnipper, including Fortune 500 companies, government agencies and all Big Four audit firms. In 2025 alone, the DataSnipper platform delivered more than $1.4 billion in productivity savings to is customers. Backed by a $100 million Series B led by Index Ventures, at a $1 billion valuation, and as proud members of 1% for the Planet, we care about how we grow, not just how fast.

If you want to build AI that professionals trust with work that really matters, this is your chance to help shape the future of audit and finance and do the best work of your career along the way.

WHAT YOU'LL OWN

• 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.

WHO YOU ARE

• 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.

WHAT YOU'LL BRING

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)

WHAT WE OFFER

• 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.

RECRUITMENT STEPS

• Recruiter screen
• Hiring Manager interview
• Assessment interview
• Final interview with Engineering leadership

At DataSnipper, we believe great ideas come from different perspectives, experiences, and backgrounds. We are committed to building an inclusive workplace and providing every candidate with a fair and equitable hiring experience.

Our hiring decisions are merit-based and grounded in the skills, experience, potential, and capabilities relevant to the role.

We welcome applicants regardless of age, race, ethnicity, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or any other characteristic that makes you, you.

What matters to us is what you can bring to the role, the team, and DataSnipper. 🚀