Data Engineer

• Own the event ingestion architecture end to end - Azure Event Hub, Snowpipe, Fivetran, and our shared Python/TypeScript event client libraries
• Build and operate dbt transformation pipelines that stay reliable as volume, source count, and model complexity grow
• Define and enforce event contracts and schemas so product teams can instrument new features without silent breakage downstream
• Build reverse ETL and activation paths that push modeled data back into the tools the business works in - HubSpot properties and rollups, MongoDB, Postgres, and GTM reporting

Modeling & Data Quality

• Evolve the core data models (event, user, license, company) that everything else depends on
• Own Snowflake performance and cost, and keep the platform’s tech debt, dependency, and compliance obligations (audit logging, vulnerability remediation, Vanta evidence) from accumulating
• Integrate and model new data sources across the business - product backends, MongoDB, HubSpot, billing, and third-party tools, * Build the guardrails and tooling that let product teams create events, models, and dashboards themselves
• Contribute to the semantic / context layer so metrics have one agreed definition across BI tools, customer-facing dashboards, and LLM and agent consumers
• Support the customer-facing analytics surfaces (in-product dashboards, standard and advanced data exports) with the aggregation and modeling work behind them
• Improve documentation and definitions to the point where analysts, stakeholders, and AI agents can self-serve with confidence
• Partner with Product, Engineering, CS, and GTM to turn vague data requests into scoped, well-defined work - and to push back when a request shouldn’t become a pipeline

• 7+ years in data engineering or a closely related backend/platform role, with a track record of owning a data platform area end to end
• Deep SQL and strong Python, including query optimization and performance tuning on a cloud warehouse
• Production experience with a cloud data warehouse (we use Snowflake) and a modern transformation framework (we use dbt)
• Experience with event-driven / streaming ingestion and the failure modes that come with it (schema drift, duplication, late data, backfills)
• Experience on a cloud platform at the infrastructure level (we’re on Azure; AWS/GCP transfers fine)
• Solid data modeling fundamentals and the ability to defend a modeling decision to both engineers and business stakeholders
• Excellent communication in English and genuine comfort working directly with non-technical stakeholders
• Experience in a startup or scale-up, especially as an early member of a data team
• Bias to action, sense of ownership, and the judgment to prioritize independently when demand exceeds capacity, * Experience with product analytics tooling (Mixpanel, RudderStack) and warehouse-native BI (Netspring/Optimizely Analytics, Omni, Embeddable, or similar)
• Experience building data products for AI or agent consumption - semantic layers, metrics layers, MCP servers, or governed self-service access
• Experience with Terraform, Docker, and governance at scale
• Reverse ETL experience and familiarity with CRM data models (HubSpot, Salesforce) or customer success platforms
• Exposure to B2B SaaS usage-based pricing and entitlement data, or to audit/fintech

• Being part of one of the fastest-growing scale-ups in the Netherlands
• Make an impact by disrupting the audit industry with us
• 28 vacation days
• Excellent salary
• Pension plan
• Stock participation plan
• Hybrid work (Amsterdam-based)
• International team and environment
• Daily lunch ️
• Mental health support (OpenUp)
• Social events and team activities

Recruitment steps

• Recruiter screen
• Hiring Manager interview
• Peer programming session
• System design interview
• Final interviews with Engineering leadership

Every decision DataSnipper makes about its products - which features land, which customers are getting value, what we bill for, what we fix next, which AI capabilities add the most value - runs through the data platform. You will own the systems that make that possible: how usage events get captured across a growing set of products, how they become trustworthy models in Snowflake, and how every other team such as Customer Success, Product, and GTM teams get to the answers without waiting on us.

This is a hands-on, high-ownership role in a small team. We are a handful of people serving the whole company, so your judgment about what not to build matters as much as what you ship. You will set the technical direction for ingestion and modeling, and you will be the person other engineering teams come to when they need to instrument something new.

About DataSnipper

DataSnipper is the driving force behind an intelligent automation platform that’s transforming the world of audit and finance.

Founded in 2017, DataSnipper has skyrocketed and is now OFFICIALLY the fastest-growing software company in the Netherlands according to Deloitte Fast50 and recently achieved Unicorn status in our latest funding round. With over 400.000 users in 125+ countries and a second base in the heart of New York City, DataSnipper is shaking things up. And we’re not stopping there. At DataSnipper, we’re always on the lookout for innovators who think outside of the box. New ideas aren’t just welcomed at DataSnipper-they’re essential.

What You Will Own

The Data Platform team works across three areas, and this role sits closest to the first two:

• Data Platform - reliable, scalable infrastructure that gets the right data to the right place
• Internal Analytics - a self-service platform so every team can be data-informed without a ticket
• Customer-facing Analytics - the dashboards and exports customers use to see the value they get from DataSnipper

Concretely, you’d be walking into: billions of usage events flowing from our Excel Add-in, web apps, and product backends through Azure Event Hubs into Snowflake; a dbt estate built on medallion principles and managed in dbt Cloud; Terraform-managed Snowflake and Azure infrastructure; and a set of product teams shipping AI agents faster than we can instrument them.

You will also find real, named open problems rather than a tidy platform - event capture mid-consolidation, multiple methods of user attribution, and a data quality layer that is designed but not yet built. We would rather tell you that up front.