Analytics Engineer

About Planet   Planet is a global provider of integrated technology and payments solutions for retail and hospitality customers. We create great experiences for the millions of people who use our payments, software, and tax-free solutions every minute of every day. Planet empowers its customers to deliver great customer experiences by combining payments and software in ways that drive greater loyalty, increase revenue and save time.   Founded over 35 years ago and with our headquarters in London, today we have more than 2,500 employees located across six continents serving our customers in more than 120 markets.   Role overview: As an Associate Analytics Engineer, you will take increased ownership in building and maintaining analytics‑ready data models that support reporting, dashboards, and business decision‑making. This role builds on foundational analytics engineering skills and focuses on independent delivery, data quality, and consistent application of best practices using dbt, SQL, and Snowflake. You will work closely with Senior Analytics Engineers, Data Analysts, Data Engineers, and business stakeholders to transform raw data into clean, tested, and well‑documented datasets. Compared to the AAE role, this position requires less day‑to‑day guidance and greater accountability for the quality and reliability of delivered models. The AAE II is expected to be a reliable individual contributor, capable of owning well‑scoped analytics transformations end‑to‑end while continuing to grow toward senior‑level responsibilities. What you will do Design & Develop Analytics Models Independently develop Bronze → Silver dbt models following established standards. Contribute to Gold‑layer models and analytics outputs under guidance. Apply dimensional modeling concepts (facts, dimensions) to support analytics and BI use cases. Data Transformation & dbt Development Use dbt to build, test, and document analytics models. Refactor existing models to improve clarity, performance, or consistency. Apply reusable patterns and macros where appropriate. Data Quality & Testing Implement appropriate dbt tests (e.g. not null, uniqueness, accepted values) for all owned models. Validate data outputs against business logic and expected behaviour. Identify potential data quality issues early and work with peers to resolve them. Performance & Reliability Be mindful of model performance and warehouse costs. Apply basic optimisation techniques (e.g. filtering, incremental models) with guidance. Monitor model runs and respond to failures or anomalies. Collaboration & Stakeholder Support Work closely with analysts to understand reporting and data requirements. Support analytics use cases by ensuring data is well‑structured and clearly defined. Communicate progress, risks, and blockers proactively. Documentation & Knowledge Sharing Own documentation for delivered models and columns using dbt. Write clear, business‑friendly…