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.

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.

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.

Eye Security is hiring an Analytics Engineer (f/m/x)