Senior Technical Product Manager — Document Intelligence

Stack

Responsibilities

• ▹Own the document intelligence pipeline: technical direction for how unstructured documents are ingested, parsed, extracted, classified and summarized
• ▹Own the product strategy for the platform, translating customer and agent-team needs into requirements and a tooling roadmap (APIs, workflows, internal platforms)
• ▹Define how processed documents and derived data are stored, indexed and served so agentic flows can retrieve the right information quickly and reliably
• ▹Own the platform's non-functional requirements: extraction accuracy, throughput, latency, cost per document, reliability, scalability, observability
• ▹Create and run the evals that measure extraction, classification and summarization quality, defining the quality bar before shipping, in partnership with the LLM Ops team
• ▹Drive model and tooling strategy: models, extraction techniques, build-vs-buy, cost/performance/accuracy trade-offs
• ▹Partner cross-functionally: technical peer to engineering managers and tech leads, serving the agent teams and the Integrations team

Requirements

• ▹4+ years in product management, with 2+ years on technical, platform or 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 and NLP techniques, with enough grasp of LLM-based approaches to judge when they fit
• ▹Background in distributed systems, data infrastructure or pipelines, and how processing systems behave at scale
• ▹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 and prototype with code to validate hypotheses

Nice 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
• ▹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

Soft skills

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)
• ▹Pension plan with a 6% contribution on top of base salary
• ▹28 vacation days per year (full-time)
• ▹Hybrid work model with at least 3 days onsite in the Amsterdam office
• ▹Daily freshly prepared lunches by an in-house chef
• ▹NS business card for commuting
• ▹Structured onboarding programme with dedicated time to learn the product and customers
• ▹Continuous learning and development initiatives
• ▹International team spread across seven global offices
• ▹Company-wide events like DataSnipper GO
• ▹Access to OpenUp, a mental health and wellness platform

About the company

DataSnipper is a bootstrapped unicorn with 600,000+ users across 180+ countries, used by top audit and accounting firms. Its Excel Agent brings AI directly into Excel, working with real documents and audit evidence with full traceability.