D
DataSnipper
Data Engineering Team Lead
Skills
SQL QueriesMongoDBData EngineeringDockerTypescriptPythonPostgreSQLSnowflakeData AnalysisAICRMHubspotComplianceTerraformAzure
5 days ago
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Important information
Contract type:
Permanent
Location:
Netherlands
Work mode:
Onsite
Published on:
17 October 2026
What they need
- 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- Recruiter screen
- Hiring Manager interview
- System design interview
- Product interview
- Final interviews with Engineering leadership
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