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.

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 your team owns

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.

What you will do

Lead the team

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

Own direction and delivery

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

Stay hands-on in the data platform

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

What you bring

Leadership

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

Data engineering

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