Analytics Engineer

• Lead and grow a team of analytics engineers (currently 2, scaling to 4 this year), building a culture of craft, documentation, and user empathy
• Drive the rollout and adoption of Lightdash as our single source of truth for business reporting, based on a unified KPI framework currently in progress
• Own all dashboard development initially - from executive reporting to operational views, with support from analysts - then fully transition the ownership to analysts as self-service matures, building the templates and processes that enable this shift
• Partner with stakeholders to translate reporting needs into well-designed, maintainable data products
• Design and deliver training and enablement programs for business users across all functions
• Own and evolve our core dbt models and semantic layer to support key analytical use cases: customer LTV, acquisition effectiveness, retention, funnel performance, and financial reporting
• Establish governance and standards: metric definitions, dashboard design patterns, modeling practices, testing frameworks, and documentation
• Partner with analysts to translate their needs into scalable data assets, and with Data Engineering on pipeline reliability and data quality
• Partner with Data Engineering on pipeline reliability, data quality, and infrastructure decisions
• Balance rigour with delivery speed-we're still building foundations while the business moves fast

Analytics Engineering Manager

• Design, build, and maintain reliable, tested, and well documented ETL and ELT pipelines and DBT models
• Work with stakeholders across the business to understand data needs and translate them into high quality, reusable data products
• Identify gaps and improvement areas across the data ecosystem and shape the analytics engineering roadmap
• Build and maintain self-service tools and datasets that allow engineers, data scientists, and analysts to get to insights quickly
• Partner with Product Engineering, Data Science, and Analytics teams to enable effective use of the data platform
• Investigate and resolve data related bugs and technical issues in platform tooling and DBT models
• Contribute to and lead cross-team data projects with a focus on reliability, efficiency, and testability

• Take ownership of end-to-end data projects, from problem definition to deployment and monitoring
• Analyze player behavior to understand game performance, progression, monetization, and engagement drivers
• Define KPIs in collaboration with business stakeholders and ensure their robust and consistent implementation in data models
• Design and optimize data models and datasets tailored to analytical and business needs
• Ensure data quality and consistency, validating assumptions and identifying data issues
• Leverage and adapt AI tools and approaches to enhance data workflows and impact
• Demonstrate autonomy and decision-making, driving projects forward in ambiguous environments
• Contribute to the data roadmap, identifying high-impact opportunities for data science and analytics

• Diseñar y mantener modelos de datos analíticos en DBT, asegurando la trazabilidad, eficiencia y calidad de los datasets
• Construir y optimizar consultas SQL avanzadas en BigQuery, incluyendo la creación de tablas, vistas, rutinas y queries programadas (scheduled queries)
• Definir, estandarizar y documentar métricas y KPIs para garantizar su consistencia en toda la organización
• Colaborar con los equipos de negocio para entender necesidades analíticas y traducirlas en modelos de datos escalables y reutilizables
• Desarrollar y mantener dashboards y visualizaciones en Metabase y Tableau, asegurando su alineación con los modelos analíticos
• Implementar procesos de testing y validación de datos (data quality) dentro de DBT
• Contribuir a la mejora continua de la arquitectura de datos junto al Senior Data Engineer
• Participar en la definición de buenas prácticas de versionado, testing y documentación en Git

• Reingeniería completa del procesamiento de datos, abordando la modernización de la arquitectura existente.
• Colaborar de forma continua con negocio en la toma de requisitos y su traducción a soluciones técnicas.
• Diseñar y desarrollar procesos de extracción, ingesta, almacenamiento y transformación de datos.
• Garantizar la correcta construcción de la capa analítica y la provisión de información para Power BI.
• Participar en la definición y ejecución de la estrategia de evolución tecnológica, incluyendo la migración progresiva de Databricks.
• Coordinar la transición hacia arquitecturas basadas en AWS y Microsoft Fabric.
• Liderar el desmontaje progresivo de procesos Spark en favor de soluciones basadas en bases de datos SQL.
• Asegurar la calidad, fiabilidad y escalabilidad de los datos utilizados por los equipos de revenue y hotel.

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