Analytics Engineer (f/m/x) - Remote Eligible

Analytics Engineer

Eye Security is providing cybersecurity with embedded cyber insurance solutions for organizations in Europe. Headquartered in the Netherlands, we are already over 170 FTEs and continue to grow internationally.

We combine cutting-edge technology with hands-on expertise to detect, respond to, and recover from cyber threats in real time. Our team brings together talent from intelligence, military, tech, and consulting backgrounds — all united by a shared mission: to make enterprise-grade cybersecurity accessible to every business, not just the big players.

At Eye, you'll work on projects with an international footprint, solving real-world challenges and helping to build a safer digital future for our clients.

We are looking for an Analytics Engineer (f/m/x) to join our data team and help turn Eye Security's data into decisions that move the business. You will work alongside our senior data and analytics engineers to build and maintain the analytics layer that teams across the company rely on every day.

This is a role focused on solid execution and continuous growth. You will own well-scoped analytics projects end-to-end, partner directly with business stakeholders, and grow into broader ownership over time with direct mentorship from the senior engineers on the team.

You will be part of a small, focused data team that treats analytics as a product. Our goal is not just technically correct models but ones that actually impact how the business makes decisions.

What you will do

• Design, build, and maintain dbt models that power the dashboards and metrics the business runs on.
• Partner with stakeholders across the business to clarify what they actually need — not just what they asked for — and translate it into reliable analytics products.
• Build and improve Metabase dashboards and questions, and help drive self-serve adoption across the company.
• Help raise the bar on our modeling standards: naming, testing, documentation, and how we layer the warehouse.
• Debug data quality issues end-to-end. When a model fails or a stakeholder reports inaccuracies you own the debugging from source system through transformation to the dashboard a stakeholder is staring at.
• Participate in design discussions, pair with senior engineers, and learn how we make technical decisions.
• Play an active role in onboarding new colleagues, and mentor more junior engineers as the team grows.

What you will need

• 3–5 years of hands-on experience in analytics engineering, BI engineering, or a similar data role.
• Strong SQL. You can write clean, performant queries, debug someone else's, and structure models others can build on without wincing.
• Working proficiency with dbt. You understand model layering, tests, macros, and how to keep a project maintainable past the first six months.
• Solid grasp of data modeling principles (dimensional modeling, SCDs, grain) and the judgment to know when to follow them and when not to.
• Comfortable with a modern cloud warehouse (we use Snowflake). You can read a query plan well enough to know why something is slow.
• Experience building dashboards in a BI tool (we use Metabase). You can tell a useful dashboard from a decorative one.
• Comfortable working in a Git-based workflow. Branching, reviewing, and merging without drama.
• Ability to work directly with non-technical stakeholders. You ask clarifying questions when requirements are vague rather than guessing and rebuilding later.
• Strong debugging instincts. You can take a report like "the revenue number looks off" and actually chase it back to the source.
• Ability to work independently on well-scoped tasks and communicate progress without being chased.
• Good written and verbal English; comfort working across multiple stakeholder teams.
• Comfort in a scale-up environment where priorities shift and context evolves.

Nice to have

• Python for ingestion of data, transformation, ad-hoc analysis, or scripting.
• Practical understanding of LLMs and agent-based workflows. You've used them for real work, not just demos, and have a sense of where they help and where they don't.
• Experience with a data glossary, catalog, or metric layer tool.