---
title: 'Senior Technical Product Manager — Agent Runtime at DataSnipper'
canonical: 'https://feeny.ai/job/senior-technical-product-manager-agent-runtime-datasnipper-amsterdam-6q1v7wbwmt9g'
type: 'job'
last_seen: '2026-09-06'
---

# Senior Technical Product Manager — Agent Runtime at DataSnipper

- **Company:** DataSnipper
- **Location:** Amsterdam, Netherlands
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-12
- **Last confirmed live:** 2026-09-06
- **Apply:** https://jobs.ashbyhq.com/datasnipper/ea4bb202-d0c8-40e2-868d-0ee6f5b80f65/application

**Skills:** LLM-based systems, Agent architectures, Prompting, Tool use, Planning, Multi-step agent loops, Context and memory management, Multi-agent systems, Retrieval/vector search, Distributed systems, Infrastructure, Non-functional requirements, Latency, Reliability, Scalability, Cost optimization, Observability, Telemetry, Model operations, Model selection

> Own the agent runtime architecture and non-functional requirements for DataSnipper's agentic products. Define technical direction for orchestration, reliability, and cost-efficiency while leading product evaluations and model strategy.

## Job description

## ABOUT THE ROLE

We're looking for a Technical PM to own the agent runtime and the platform that powers our agentic products — and the non-functional requirements that make those agents accurate, fast, reliable, and cost-effective at scale.

You'll own how well the platform runs, not the end-user features on top of it. You'll define the runtime architecture, own the product evals that prove our agents stay accurate as we scale and upgrade models, and set the bar for latency, reliability, cost, and observability across our agent runtimes.

This is an engineering-facing role — but it's a product role at heart. You translate what customers and the business actually need into the architecture that delivers it: product judgment expressed through technical decisions. You'll spend your time in architecture discussions and eval design alongside ML and backend engineers, not in design reviews. When you work with go-to-market, it's to turn runtime scale, stability, and reliability into a story the market trusts. And you'll track how the frontier of agentic engineering is moving — keeping us ahead of it and being a visible voice on it inside and outside the company.

If you've never written a prompt chain, debugged a pipeline, reasoned about token cost, or designed an eval, this isn't the right fit.

## 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 OWN

Agent runtime architecture. Technical direction for our agent runtimes: agent orchestration and multi-step loops, context and memory management, retrieval strategy, tool execution and orchestration, model routing and fallbacks, guardrails, caching, streaming, and concurrency.

Non-functional requirements of the platform. How well the platform runs - latency, throughput, reliability, graceful degradation, scalability under concurrent agent load, token-cost economics, security, and observability/telemetry. These are your primary success metrics, not feature counts.

Product evals. Own the creation and running of the evals that prove our agents are accurate and reliable - defining the quality bar every agent meets before it ships, and the measures that let us improve and upgrade models with confidence. 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 strategy. Model selection, swaps, and provider decisions; cost/performance trade-offs; staying current as frontier models move.

Market intelligence & thought leadership. Track how the field is building agentic systems. Own a point of view on what differentiates us and where the runtime must go to stay ahead - and evangelize it both internally and externally (talks, writing, customer and industry conversations).

Cross-functional partnership. Engineering is your primary partner - you operate as a technical peer to engineering managers and tech leads, not just a prioritization partner. You translate experience-team needs into runtime capability, and partner with go-to-market on scale, stability, and reliability.

## WHO YOU ARE

- A tinkerer. You build to understand - prototypes, prompt chains, quick experiments. You'd rather try it 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 understanding into strong architecture. The technical depth is in service of product value, not an end in itself.
- Highly autonomous. You operate with little direction: you find the problems that matter, set the direction, and drive them without waiting to be told.
- Entrepreneurial. You treat your area like your own company - scrappy, outcome-obsessed, and comfortable making the call under ambiguity.

## WHAT WE'RE LOOKING FOR

Must-have:

- 4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)
- Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool
- Background in distributed systems or infrastructure - you understand how runtimes behave under scale and how a change ripples across the stack
- Treats non-functional requirements as a first-class product surface: latency, reliability, scalability, cost, observability
- Hands-on experience creating and running evals that measure and improve agent accuracy and reliability at scale
- Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimization
- Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (latency vs. accuracy, cost vs. capability)

Strong-to-have:

- Former software engineer, ML engineer, or data scientist who moved into product
- A public point of view on AI/agents - writing, talks, OSS - or the appetite to build one
- Experience in audit, accounting, or financial services (domain context for what the agents do)

## 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.

## About DataSnipper

## Company Overview
- **One-liner**: DataSnipper provides an AI‑powered agentic platform that automates repetitive verification and testing workflows directly inside Excel for audit and finance teams.
- **Entity Type**: Private (Series B, valued at $1 billion as of February 2024)
- **Headquarters**: Amsterdam, Netherlands (with offices in New York and Kuala Lumpur)
- **Founded**: 2017
- **Founders**: Maarten Alblas, Jonas Ruyter, Kai Bakker

## Core Business
- **Primary industry**: Financial audit & accounting technology; intelligent automation and AI for enterprise compliance
- **Target customers**: B2B – audit and finance teams at top accounting firms and Fortune 500 companies
- **Mission**: “Disrupt the audit and finance industry” by eliminating manual, repetitive tasks and empowering professionals to focus on high‑judgment work.

## Products & Services
- **DataSnipper Agentic Platform**: An AI‑driven platform that uses agents to run end‑to‑end workflows (e.g., royalty calculations, revenue testing, test of details). Agents handle the repetitive work while reviewers approve outputs.
- **AI Suite**: Analyzes and surfaces insights from unstructured documents using natural‑language queries.
- **Cloud Collaboration Suite**: Real‑time collaboration for distributed teams, integrated directly with Excel.
- **Invoice / Receipt / Contract Extraction**: Automated data extraction from invoices, receipts, and contracts using OCR and AI.
- **WebSnip**: Extracts and validates data from webpages into Excel (text snip, table snip).
- **Excel Agents**: Automates analysis and testing directly inside Excel (announced recently).

## Market Standing
- **Valuation**: $1 billion (unicorn status as of Feb 2024)
- **Total Funding**: €92 million Series B (≈$100 million) raised in Feb 2024
- **Notable Investors**: monashees, Index Ventures (and 3+ investors per Crunchbase)
- **Growth Signals**:
  - Trusted by all top 100 accounting firms and Fortune 500 companies they serve.
  - 85% time saved in high‑volume testing scenarios (FGMK case study).
  - 90% faster document reviews (Kushner LaGraize case study).
  - 10 000 data points validated across source documents (Walker & Associates case study).
  - Team of 200+ professionals from 40+ nationalities, with offices in Amsterdam, New York, and Kuala Lumpur.

## Competitive Advantages
- Deep integration with Excel – the tool that audit professionals already use daily.
- “Agentic” platform that closes the verification gap by automating end‑to‑end workflows while keeping humans in the loop.
- SOC 2 compliant, strict vendor management, no training on customer data, and encryption – meeting the highest security standards.
- Strong network effects: already adopted by all top accounting firms and Fortune 500 companies, creating a switching cost.

## Strategic Focus
- Expand the “Agentic Platform” to handle more complex audit and finance workflows (internal audit, tax, financial control, government audit, advisory).
- Continue innovating with AI agents that “do the leg work” so teams can scale without adding headcount.
- Grow the engineering and product teams to accelerate product development.

## Why Work Here
- **Culture**: EMPWR values – Exceed Customer Expectations, Master Ownership, Power in Partnership, Welcome the Unknown, Results at Speed. Emphasis on collaboration, ownership, and customer‑centricity.
- **Work environment**: Modern offices with waterside views (Amsterdam), healthy lunches, ping‑pong, pool table, cozy fireplace and bar. 50% of employees come by bike; 70% join Friday drinks.
- **Perks**: 28 vacation days (full‑time), competitive salaries, solid pension, stock participation plan. Regular social activities.
- **Growth**: “One of the fastest‑growing scale‑ups in the Netherlands” – opportunities for rapid career advancement and skill development.
- **Diversity**: 40+ nationalities, inclusive and international atmosphere.
- **Sustainability**: Committed to 1% for the Planet and actively works to make a positive environmental impact.
- **Work policy**: Multiple offices; hybrid likely, but not explicitly stated. Open positions span Engineering, Product, Marketing, Customer Success, etc.

## Sources
1. [careers.datasnipper.com](https://careers.datasnipper.com/)
2. [datasnipper.com](https://www.datasnipper.com/)
3. [careers.datasnipper.com/culture](https://careers.datasnipper.com/culture)
4. [crunchbase.com](https://www.crunchbase.com/organization/datasnipper)
5. [jobs.ashbyhq.com/datasnipper](https://jobs.ashbyhq.com/datasnipper.com?urlHash=sVU8)

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