# Team Lead, Data Engineering

[Datasnipper](https://gurify.com/jobs?q=Datasnipper) · Amsterdam · Posted 4 weeks ago

Lead

[Engineering Manager](https://gurify.com/jobs/engineering-manager)

[Data](https://gurify.com/jobs/data)

[Apply on the original posting → (opens in a new tab)](https://jobs.ashbyhq.com/datasnipper/620f3ac0-99b1-46c8-9aad-1ac6161671db)

## Job description

### ABOUT THE ROLE

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.

WHY DATASNIPPER?

The hardest AI problems aren't about getting an answer fast. They're about getting one people can trust.

We believe trust is essential to how society works. Every day, we rely on information that's been independently verified, from financial statements to the products we buy, to the sustainability claims companies make. Behind that trust are audit and finance professionals who examine evidence, verify information and hold organizations accountable.

At DataSnipper, we build AI solutions that empower those professionals. By making evidence easier to trace and verify, we help them focus on the judgment and expertise that make their work so important.

For our product and engineering team, this is where things get exciting. In audit, a plausible answer isn't good enough. Every output has to be explainable and traceable back to its source. We've just launched a brand new agentic platform, Alwin by DataSnipper. AI agents running in the cloud across hundreds of documents at once, reasoning across PDF, Word and Excel, with a full audit trail behind every step. There are lots of greenfield problems ahead, and real room to shape what we build and how we build it.

And we build at scale. More than 600,000 professionals across 3,000+ organizations in 175 countries rely on DataSnipper, including Fortune 500 companies, government agencies and all Big Four audit firms. In 2025 alone, the DataSnipper platform delivered more than $1.4 billion in productivity savings to is customers. Backed by a $100 million Series B led by Index Ventures, at a $1 billion valuation, and as proud members of 1% for the Planet, we care about how we grow, not just how fast.

If you want to build AI that professionals trust with work that really matters, this is your chance to help shape the future of audit and finance and do the best work of your career along the way.

### 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 to both engineers and business stakeholders

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

### HOW YOU WORK

- 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

### PREFERRED QUALIFICATIONS

- 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

### WHAT WE OFFER

- 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

### RECRUITMENT STEPS

1. Recruiter screen (30 minutes, online)

2. Hiring Manager interview (1 hour, online)

3. System design interview (1 hour, on-site)

4. Product interview (1 hour, online)

5. Final interviews with Engineering leadership (30 mins, online)

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

**Live in Datasnipper’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role.

We remove it as soon as it disappears at source.

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Backed by a $100 million Series B led by Index Ventures, at a $1 billion valuation, and as proud members of 1% for the Planet, we care about how we grow, not just how fast.\u003C/p\u003E\u003Cp\u003EIf you want to build AI that professionals trust with work that really matters, this is your chance to help shape the future of audit and finance and do the best work of your career along the way.\u003C/p\u003E\u003Ch3\u003EWHAT YOUR TEAM OWNS\u003C/h3\u003E\u003Ch3\u003EThe Data Platform team works across three areas:\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EData Platform: reliable, scalable infrastructure that gets the right data to the right place\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EInternal Analytics: a self-service platform so every team can be data-informed without a ticket\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ECustomer-facing Analytics: the dashboards and exports customers use to see the value they get from DataSnipper\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003EConcretely, 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.\u003C/p\u003E\u003Cp\u003EYou 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.\u003C/p\u003E\u003Ch3\u003EWHAT YOU WILL DO\u003C/h3\u003E\u003Ch3\u003ELEAD THE TEAM\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003ELead a team of two data engineers through outcomes, not tasks: set clear goals with the team and trust them to figure out how\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMake the team measurably better: raise the engineering bar, grow each engineer into bigger problems, and address performance issues directly when they arise\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ERun regular 1:1s, give clear and timely feedback, and own each engineer\u0026#39;s goals and development\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EFoster a culture of experimentation, learning, documentation and operational excellence\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ETake part in hiring and help shape the team\u0026#39;s scope as it evolves\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EOWN DIRECTION AND DELIVERY\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EPartner closely with Product, Engineering, Customer Success and GTM to shape the team\u0026#39;s roadmap, turning vague data requests into scoped, well-defined work\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMake the trade-offs a small team serving the whole company needs: what to build, what to defer, and when a request shouldn\u0026#39;t become a pipeline\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESet the technical direction and make the architectural calls that shape the team\u0026#39;s systems\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EKeep Snowflake performance and cost, tech debt, dependencies and compliance obligations (audit logging, vulnerability remediation, Vanta evidence) visible and under control\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBe the person other engineering teams come to when they need to instrument something new\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ESTAY HANDS-ON IN THE DATA PLATFORM\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EContribute to the event ingestion architecture end to end: Azure Event Hubs, Snowpipe, Fivetran, and our shared Python/TypeScript event client libraries\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBuild and review dbt transformation pipelines that stay reliable as volume, source count and model complexity grow\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDefine and enforce event contracts and schemas so product teams can instrument new features without silent breakage downstream\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EEvolve the core data models (event, user, license, company) and integrate new sources such as product backends, MongoDB, HubSpot and billing\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EGuide reverse ETL and activation work that pushes modeled data back into HubSpot, MongoDB, Postgres and GTM reporting\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWHAT YOU BRING\u003C/h3\u003E\u003Ch3\u003ELEADERSHIP\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EAt least 1 year leading engineers as a team lead or engineering manager: 1:1s, feedback, goal setting, performance, and development\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EAn empowerment style of leadership: you lead through outcomes and trust, not by assigning tasks\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EEarly evidence of growing engineers and raising a team\u0026#39;s bar, and the willingness to address performance issues directly\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong product aptitude: you can challenge the framing of a request, translate stakeholder needs into technical solutions, and make roadmap trade-offs\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EDATA ENGINEERING\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003E7\u002B years in data engineering or a closely related backend/platform role, with a track record of owning a data platform area end to end\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDeep SQL and strong Python, including query optimization and performance tuning on a cloud warehouse\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EProduction experience with a cloud data warehouse (we use Snowflake) and a modern transformation framework (we use dbt)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with event-driven / streaming ingestion and its failure modes: schema drift, duplication, late data, backfills\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience on a cloud platform at the infrastructure level (we\u0026#39;re on Azure; AWS/GCP transfers fine)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESolid data modeling fundamentals and the ability to defend a modeling decision to both engineers and business stakeholders\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience operating data systems in production with a focus on reliability, observability and maintainability\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EHOW YOU WORK\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EExcellent communication in English and genuine comfort working directly with non-technical stakeholders\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBias to action, a strong sense of ownership, and the judgment to prioritize when demand exceeds capacity\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EPREFERRED QUALIFICATIONS\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EExperience in a startup or scale-up, ideally as an early member of a data team\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience hiring engineers or helping a team expand its scope\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with product analytics tooling (Mixpanel, RudderStack) and warehouse-native BI (Netspring/Optimizely Analytics, Omni, Embeddable, or similar)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience building data products for AI or agent consumption: semantic layers, metrics layers, MCP servers, or governed self-service access\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience with Terraform, Docker and data governance at scale\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EReverse ETL experience and familiarity with CRM data models (HubSpot, Salesforce) or customer success platforms\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003EWHAT WE OFFER\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EBeing part of one of the fastest-growing scale-ups in the Netherlands\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMake an impact by disrupting the audit industry with us\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EA step into engineering management, with Engineering Manager scope on a small team\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003E28 vacation days\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExcellent salary\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EPension plan\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStock participation plan\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EHybrid work (Amsterdam-based)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EInternational team and environment\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EDaily lunch\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EMental health support (OpenUp)\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003ESocial events and team activities\u003C/li\u003E\u003C/ul\u003E\u003Ch3\u003ERECRUITMENT STEPS\u003C/h3\u003E\u003Cp\u003E1. 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We are committed to building an inclusive workplace and providing every candidate with a fair and equitable hiring experience.\u003C/p\u003E\u003Cp\u003EOur hiring decisions are merit based and grounded in the skills, experience, potential, and capabilities relevant to the role.\u003C/p\u003E\u003Cp\u003EWe 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.\u003C/p\u003E\u003Cp\u003EWhat matters to us is what you can bring to the role, the team, and DataSnipper\u003C/p\u003E","identifier":{"@type":"PropertyValue","name":"Gurify","value":"team-lead-data-engineering-at-datasnipper-bb00196c42f3"},"url":"https://gurify.com/job/team-lead-data-engineering-at-datasnipper-bb00196c42f3","datePosted":"2026-10-14","validThrough":"2026-12-26T23:59:59Z","hiringOrganization":{"@type":"Organization","name":"Datasnipper","sameAs":"https://jobs.ashbyhq.com/datasnipper"},"directApply":false,"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressCountry":"NL","addressLocality":"Amsterdam"}}}
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