Data Engineering Team Lead

Datasnipper; Netherlands; On-site; EUR 90,000 - 130,000; Full time

Job summary

DataSnipper is seeking a Data Engineering Team Lead to steer the Data Platform and guide a small, high-ownership team of two engineers. You own the roadmap, make architectural calls, and stay hands-on enough to ship alongside the team.

This player-coach role emphasizes delivering measurable impact across data capture, modeling and utilization while partnering with Product, Customer Success and GTM to turn data requests into scoped work.

Qualifications

• 7+ years in data engineering or a closely related backend/platform role with end-to-end ownership.
• Experience leading engineers as a team lead or engineering manager with 1:1s and performance feedback.
• Production experience with a cloud data warehouse and modern transformation framework.
• Strong product aptitude to translate stakeholder needs into technical solutions.

Responsibilities

• Lead a two-person data engineering team, setting goals and guiding execution.
• Own the Data Platform roadmap, architecture, and technical direction.
• Collaborate with Product, Customer Success and GTM to shape data-driven initiatives.
• Instrument data pipelines end-to-end, ensure reliability, observability and cost controls.
• Hire, develop and coach engineers, raise the team bar and address performance issues.

Skills

Data platform ownership

Team leadership

Architecture decisions

SQL proficiency

Python

Stakeholder management

Cloud data warehouse

dbt / transformation pipelines

Event-driven architectures

Data modeling

Tools

Terraform

Docker

dbt Cloud

Job description

• 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
• The Data Platform team works across three areas: reliable, scalable infrastructure that gets the right data to the right place; a self-service platform so every team can be data-informed without a ticket; and 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

• Experience on a cloud platform at the infrastructure level
• 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
• Bias to action, a strong sense of ownership, and the judgment to prioritize when demand exceeds capacity
• Excellent communication in English and genuine comfort working directly with non-technical stakeholders
• At least 1 year leading engineers as a team lead or engineering manager, including 1:1s, feedback, goal setting, performance, and development
• Production experience with a cloud data warehouse and a modern transformation framework
• 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
• Strong product aptitude: you can challenge the framing of a request, translate stakeholder needs into technical solutions, and make roadmap trade-offs
• Solid data modeling fundamentals and the ability to defend a modeling decision to both engineers and business stakeholders
• Experience with event-driven / streaming ingestion and its failure modes: schema drift, duplication, late data, backfills
• Experience operating data systems in production with a focus on reliability, observability and maintainability
• Deep SQL and strong Python, including query optimization and performance tuning on a cloud warehouse
• Experience hiring engineers or helping a team expand its scope
• Experience with Terraform, Docker and data governance at scale
• Experience with product analytics tooling and warehouse-native BI
• Reverse ETL experience and familiarity with CRM data models or customer success platforms
• Experience building data products for AI or agent consumption: semantic layers, metrics layers, MCP servers, or governed self-service access
• Experience in a startup or scale-up, ideally as an early member of a data team