Data Engineer

Experteer Overview Asegúrese de enviar su solicitud rápidamente para maximizar sus posibilidades de ser considerado para una entrevista.Lea la descripción completa del puesto a continuación.In this role you will design and build batch and near-real-time data pipelines on a Databricks-based Lakehouse to enable reliable enrichment and AI-driven insights.You will work within the Data Platform and Data Enrichment team, contributing to data trust and customer-focused data products that power Discover and internal tooling.The role combines hands-on engineering with cross-pod collaboration to scale data infrastructure and improve pipeline reliability.You will be part of a mission-driven, pod-based culture that values ownership and continuous learning.This is a chance to shape scalable data foundations and enable impactful analytics for a global brand ecosystem.Compensaciones / Beneficios* Design and implement batch and near-real-time data pipelines using PySpark and Databricks across Bronze/Silver/Gold layers* Architect efficient Delta Lake table schemas, including partitioning, liquid clustering, schema evolution, and enrichment workflows* Collaborate with product, QA, and other data engineers to translate enrichment and search requirements into reliable pipelines* Own code quality with structured PySpark jobs, unit tests (pytest), and team conventions* Improve pipeline reliability and cost efficiency through scheduling optimization, retry logic, and concurrency management* Contribute to cross-pod initiatives within the data platformResponsabilidades* 3+ years of relevant work experience in a SaaS environment with distributed data processing* Strong Python and PySpark experience* Hands-on experience with Lakehouse architectures (Databricks, Delta Lake, xqbhyrx or equivalents)* Familiarity with Bronze/Silver/Gold data design patterns and schema evolution* Ability to reason about code, understand complex logic, and work with both procedural and object-oriented code* Self-motivated, adaptable, and able to thrive in a fast-paced, results-oriented setting* Fluent EnglishRequisitos principales* learning and development allowance* flexible working arrangements* remote-friendly with home office support* location-based benefits* opportunity for growth* pod autonomy

This is a chance to shape scalable data foundations and enable impactful analytics for a global brand ecosystem.Compensaciones / Beneficios* Design and implement batch and near-real-time data pipelines using PySpark and Databricks across Bronze/Silver/Gold layers* Architect efficient Delta Lake table schemas, including partitioning, liquid clustering, schema evolution, and enrichment workflows* Collaborate with product, QA, and other data engineers to translate enrichment and search requirements into reliable pipelines* Own code quality with structured PySpark jobs, unit tests (pytest), and team conventions* Improve pipeline reliability and cost efficiency through scheduling optimization, retry logic, and concurrency management* Contribute to cross-pod initiatives within the data platformResponsabilidades* 3+ years of relevant work experience in a SaaS environment with distributed data processing* Strong Python and PySpark experience* Hands-on experience with Lakehouse architectures (Databricks, Delta Lake, xqbhyrx or equivalents)* Familiarity with Bronze/Silver/Gold data design patterns and schema evolution* Ability to reason about code, understand complex logic, and work with both procedural and object-oriented code* Self-motivated, adaptable, and able to thrive in a fast-paced, results-oriented setting* Fluent EnglishRequisitos principales* learning and development allowance* flexible working arrangements* remote-friendly with home office support* location-based benefits* opportunity for growth* pod autonomy