pleo - Analytics Engineer

• Solid SQL and dbt experience, with a clear comfort owning models end to end. • Working knowledge of event-based tracking tools like Segment or Amplitude. • Experience with Git-based development workflows, including opening and reviewing pull requests. • Clear communication skills to explain data definitions and structures to both technical and non-technical stakeholders. • Genuine day-to-day use of AI tooling as part of your standard coding and workflow routine. • Comfort navigating ambiguity and untangling legacy logic to build modern foundations. • This role is NOT a good fit if • You only want to write dashboards without touching or owning the underlying data models. • You require rigid processes and heavy upfront structure before taking initiative. • You prefer to avoid data migration, cleanup work, and legacy system refactoring. • Your first 6 months • Ramp up: Master our dbt project structure, tracking plans, and migration roadmap while shipping small, reviewed code changes early on. • Take ownership: Own a dedicated slice of the migration, such as an event domain or our core experimentation data models. • Drive consistency: Establish semantic layer definitions for your domain so analysts and PMs can self-serve accurate metrics without custom workarounds. • Data warehouse: GCP / BigQuery (new Analytics Warehouse) • Transformation: dbt • Orchestration: Airflow • Tracking and Oroduct Analytics: Segment, Amplitude • BI and Analytics: Omni (migrating off legacy Looker and Hippocampus) • Languages: SQL • Last time we hired an Analytics Engineer, we received a total of 173 applications but only 10 were selected for an intro call. Some of the key reasons why previous candidates didn’t make it past the application screening stage include: • CV writing and content: it was very clear that many of the CVs we saw were very generic and AI generated. There is no issue with leveraging AI to help with CV writing, there was little indication of what real impact the candidates had in their previous experience. You might have heard of the “Achieved X, as measured by Y, by doing Z” formula (credit Laszlo Bock ~2014), this is a great way to give a clear picture of what you have actually worked on. Some link or quick description of the companies you’ve worked with is also a great help. • Application care: every single application we receive is reviewed by a human (yes, hundreds of them) because we believe that candidates' efforts should be matched by an equal level of human care. This means that we expect a similar level of attention put into your application. Read and answer the application questions carefully, they make a huge difference in our decision-making process.