Head of Data Platform

Head of Data Platform

Location: Amsterdam | Hybrid
Experience: 10+ years in data, including 5+ years leading data or analytics engineers

Build the data foundation Loop runs on

At Loop, data is how we run the business. Finance closes the books on it, Operations plans on it, Marketing and Sales steer on it, and more and more, our connected products generate it.

The Data Platform team is in good shape. It’s stable, it has doubled its delivery velocity since last year, and it works at a steady cadence. What it needs now is a permanent leader who takes it further: from a team that delivers to a platform the whole company can trust.

The Role

You’ll lead a team of about 9 people across Data Engineering, Analytics Engineering and SAP Integrations. You report to the VP Technology and partner closely with the Technology Business Partner on demand intake, prioritisation and stakeholder alignment.

You own the engineering fundamentals, the people and the platform. You’re accountable for a data foundation that is reliable, scalable and well governed, and for turning business priorities into a roadmap your team can actually deliver.

What You’ll Do

• Agree with the VP Technology how the Data Platform needs to grow over the next 1–2 years, treating data as an enabler for AI, and turn that into plans, skills and hiring.

• Grow talent and technical leadership. Coach and develop experienced engineers, strengthen technical ownership across disciplines, and build a high-performing team with clear accountability.

• Run the platform as a product together with the Technology Business Partner: keep the Now / Next / Later roadmap and delivery cadence going, hold ad-hoc work at 20% or less, and plan device telemetry and experimentation data as named work.

• Make critical pipelines, integrations and models reliable, with tests, freshness checks, monitoring and incident follow-ups, so issues are caught before a business user spots them.

• Put governance into daily practice: a clear owner for every critical dataset, one definition per core metric, access rules and standards analysts across Loop can build on.

• Run the platform as a hub-and-spoke model: you set the standards and govern the platform, while analysts across the company build on your tools and data products. Retire what nobody uses.

• Add a knowledge graph to the platform as a semantic layer, so AI works from governed business concepts rather than raw tables.

• Set the engineering bar: review code and designs, and set standards for modelling, testing and documentation across Snowflake, dbt and Dagster.

• Keep the business running on SAP, EDI and Peppol e-invoicing integrations, and make sure none of them depends on a single person.

• Own the platform budget for cloud, Snowflake and tooling, and manage external partners, including SAP partners.

• Challenge requirements, settle competing priorities, and with the Technology Business Partner help teams find and use data themselves.

You Won’t

• Run the team as a ticket queue for whoever shouts loudest.

• Ship dashboards nobody uses, or keep ones nobody needs.

• Let a business user be the first to discover a data quality issue.

• Treat governance as a policy document instead of daily practice.

• Step back from the technical work. You can review a dbt pull request, and you do.

• Leave knowledge, or an integration, in one person’s head.
  

How You’ll Succeed

You build ownership, not dependency. Engineers on your team make decisions without waiting for you, because they know what they own and what good looks like. You’d rather coach someone through a hard problem than solve it for them, and you have the difficult conversations early, when they’re still small.

Together with the Technology Business Partner you turn noise into priorities. Every department has a request, and most of them are reasonable. You translate that demand into a plan the team can actually execute, and you make the trade-offs visible: business value against delivery speed, new features against reliability, governance and technical debt. You explain those choices as clearly to an engineer as to the CFO.

And you measure yourself on what the business feels: pipelines that don’t break, metrics everyone trusts, and delivery that matches what was agreed.

What You’ll Bring

• 10+ years in software, data analytics or data engineering, including 5+ years as a line manager of data or analytics engineers, after a hands-on career as a senior engineer.

• Hands-on depth on a modern stack: a cloud warehouse (Snowflake ideally), dbt, an orchestrator and infrastructure as code.

• A track record of building data quality, testing, observability and governance into a platform.

• Analytics and data product sense: you’ve led engineers who build semantic layers, metric definitions and dashboards.

• Experience running a data platform for internal customers: roadmaps, intake, SLAs and self-service.

• A coaching leadership style, with a record of developing experienced engineers and strengthening technical ownership.

• Experience influencing senior stakeholders and owning platform budgets and vendor relationships.

• Working knowledge of ERP integrations, ideally SAP, in supply chain or finance flows.

• Fluent English. Dutch is nice to have.

It’s a big plus if you’ve built a knowledge graph as a semantic layer for AI. Scale-up experience in DTC or e-commerce (Shopify, Amazon, ad spend, contribution margin, COGS) is nice to have, as is having taken over a team from an interim manager, and led a team across countries.

What success looks like after 12 months

• A 1–2 year view of the Data Platform capability, agreed with the VP Technology.

• Tests, freshness checks and alerting on all business-critical pipelines; most incidents caught before a business user reports them.

• The delivery cadence held, with ad-hoc work at 20% or less.

• One signed-off definition for core metrics such as revenue and contribution margin, with core dashboards in measurable, growing use.

• A first version of the knowledge graph, serving at least one AI use case.

• Clear technical ownership established across Data Engineering, Analytics Engineering and SAP Integrations, and a second person behind every SAP integration.

• Platform and vendor costs tracked monthly against budget, with savings acted on.

Loop isn’t for everyone

We might not be a match if you prefer managing from a distance over staying close to the technical work, or if you see governance as paperwork rather than what makes data trustworthy. The same goes if you’d rather say yes to every request than make trade-offs visible, or if you’d rather run projects than build a platform.

Still listening?

Good. We’re not looking for someone to keep the lights on. We’re looking for someone who can turn a strong team into a data platform the whole company trusts, and that our AI can build on.

Loopers step in, speak up, and go for more. More firsts. More growth. More: “I can’t believe we did that.”

If that sounds like you, press that apply button and let’s talk.