Senior Technical Product Manager – Document Intelligence

DataSnipper

Technical PM owning the document intelligence platform at DataSnipper, turning messy documents into structured data through engineering and product strategy.

Posted 6/29/2026full-timeAmsterdam • 🇳🇱 NetherlandsSeniorWebsite

Tech Stack

Tools & technologies

Distributed Systems

About the role

Key responsibilities & impact

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.
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.
How processed documents and their derived data are stored, indexed, and served — so agentic flows can retrieve the right information quickly and reliably.
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.
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.
Engineering is your primary partner — you operate as a technical peer to engineering managers and tech leads.

Requirements

What you’ll need

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)

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Extraction TechniquesClassificationParsing/OCRSummarizationNLP TechniquesDistributed SystemsData PipelinesTechnical SpecificationsPrototypingArchitecture Trade-offs