Data Engineer
Summary
Builds batch and near-real-time data pipelines on a Databricks Lakehouse using PySpark to power AI-driven insights and reliable data products for analytics and internal tooling.
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 platform
Responsabilidades
• 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 English
Requisitos principales
• learning and development allowance
• flexible working arrangements
• remote-friendly with home office support
• location-based benefits
• opportunity for growth
• pod autonomy
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 platform
Responsabilidades
• 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 English
Requisitos principales
• learning and development allowance
• flexible working arrangements
• remote-friendly with home office support
• location-based benefits
• opportunity for growth
• pod autonomy