Analytics Engineer

About the role

Join Eye Security, a cutting-edge cyber security and insurtech organization, as an Analytics Engineer. In this role, you will work closely with senior data and analytics engineers to build and maintain the analytics layer that drives business decisions. You will own well-scoped analytics projects, partner with business stakeholders, and contribute to the overall growth of the company. Enjoy a culture-first environment, generous time off, flexible working arrangements, and numerous growth and learning opportunities. Key missions: 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 and translate it into reliable analytics products.. Debug data quality issues end-to-end and own the debugging from source system through transformation to the dashboard. Profile: - Ability to work directly with non-technical stakeholders. You ask clarifying questions when requirements are vague rather than guessing and rebuilding later - 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 - 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 - Solid grasp of data modeling principles (dimensional modeling, SCDs, grain) and the judgment to know when to follow them and when not to - Comfort in a scale-up environment where priorities shift and context evolves - 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 - Strong debugging instincts. You can take a report like "the revenue number looks off" and actually chase it back to the source - Working proficiency with dbt. You understand model layering, tests, macros, and how to keep a project maintainable past the first six months - 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