Team Lead, Data Engineering

We are looking for a Data Engineering Team Lead to lead the Data Platform team at DataSnipper.

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

This is a player-coach role in a small, high-ownership team. You are responsible for the people, the roadmap and the technical direction, and you stay hands-on enough to ship alongside the team and make architectural calls credibly. At DataSnipper, Team Lead is our entry point into engineering management: the same people, delivery and product responsibilities as an Engineering Manager, on a smaller team.

We are a handful of people serving the whole company, so your judgment about what not to build matters as much as what the team ships.

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.

The Data Platform team works across three areas:

• 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 would 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.

• Lead a team of two data engineers through outcomes, not tasks: set clear goals with the team and trust them to figure out how

• Make the team measurably better: raise the engineering bar, grow each engineer into bigger problems, and address performance issues directly when they arise

• Run regular 1:1s, give clear and timely feedback, and own each engineer's goals and development

• Foster a culture of experimentation, learning, documentation and operational excellence

• Take part in hiring and help shape the team's scope as it evolves
  

• Partner closely with Product, Engineering, Customer Success and GTM to shape the team's roadmap, turning vague data requests into scoped, well-defined work

• Make the trade-offs a small team serving the whole company needs: what to build, what to defer, and when a request shouldn't become a pipeline

• Set the technical direction and make the architectural calls that shape the team's systems

• Keep Snowflake performance and cost, tech debt, dependencies and compliance obligations (audit logging, vulnerability remediation, Vanta evidence) visible and under control

• Be the person other engineering teams come to when they need to instrument something new
  

• Contribute to the event ingestion architecture end to end: Azure Event Hubs, Snowpipe, Fivetran, and our shared Python/TypeScript event client libraries

• Build and review 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

• Evolve the core data models (event, user, license, company) and integrate new sources such as product backends, MongoDB, HubSpot and billing

• Guide reverse ETL and activation work that pushes modeled data back into HubSpot, MongoDB, Postgres and GTM reporting
  

• At least 1 year leading engineers as a team lead or engineering manager: 1:1s, feedback, goal setting, performance, and development

• An empowerment style of leadership: you lead through outcomes and trust, not by assigning tasks

• Early evidence of growing engineers and raising a team's bar, and the willingness to address performance issues directly

• Strong product aptitude: you can challenge the framing of a request, translate stakeholder needs into technical solutions, and make roadmap trade-offs

• 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 its failure modes: 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

• Experience operating data systems in production with a focus on reliability, observability and maintainability

• Excellent communication in English and genuine comfort working directly with non-technical stakeholders

• Bias to action, a strong sense of ownership, and the judgment to prioritize when demand exceeds capacity

• Experience in a startup or scale-up, ideally as an early member of a data team

• Experience hiring engineers or helping a team expand its scope

• 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 data governance at scale

• Reverse ETL experience and familiarity with CRM data models (HubSpot, Salesforce) or customer success platforms
  

• Being part of one of the fastest-growing scale-ups in the Netherlands

• Make an impact by disrupting the audit industry with us

• A step into engineering management, with Engineering Manager scope on a small team

• 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
  

1. Recruiter screen

2. Hiring Manager interview

3. System design interview

4. Product interview

5. Final interviews 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