Analytics Engineer

• Help transform data into trusted insights that drive business decisions
• Bridge the gap between data engineering and analytics
• Build scalable data models, semantic layers, and automated data pipelines that power reporting and dashboards
• Work with modern technologies including Snowflake, Power BI, dbt and AI-enabled analytics platforms
• Create a single source of truth for the business
• Design and develop analytics-ready data models and semantic layers in Snowflake
• Build scalable data transformations and automated data quality processes
• Create trusted datasets and standardised KPIs that support consistent reporting
• Partner with Data Engineers, BI Developers, and business stakeholders to deliver meaningful insights
• Drive data governance, documentation, and business-friendly data definitions
• Support self-service analytics and AI-powered reporting capabilities
• Improve data reliability, performance and automation across the analytics ecosystem

• Develop the DWH core model that transforms raw Kafka topics into documented, easy-to-use data products that bring value to data consumers
• Collaborate closely with data engineers to develop our streaming ETL framework and improve its reliability and user experience
• Help the team deliver new features and maintain the existing ones in the OpenMetadata data catalog
• Actively participate in data quality and data governance tools and processes implementations
• Partner with data consumers across the business, supporting their daily data needs and turning requirements into reliable, well-documented data products
• Tackle unique challenges that come with building a Data Platform from scratch

Senior Analytics Engineer

• Serás referencia técnica en arquitectura de medición digital, responsable de diseñar, implementar y mantener los sistemas que garantizan que el dato llega limpio, íntegro y escalable desde cualquier fuente.
• Gestionar contenedores GTM (cliente y servidor), diseñando soluciones adaptadas a las limitaciones técnicas de cada cliente.
• Implementar y resolver problemáticas en Firebase, Measurement Protocol y server-side tracking.
• Desplegar y gestionar contenedores GTM Server-Side garantizando contexto first-party y seguridad del dato.
• Modelar datos en BigQuery con SQL para optimización de costes y rendimiento.
• Desarrollar scripts para la extracción y carga automatizada de datos desde APIs de terceros.
• Garantizar la calidad del dato: integridad, ausencia de duplicidades y rupturas de sesión.
• Auditar implementaciones y mantener actualizada la documentación de la arquitectura tecnológica.

MarTech and Analytics Engineer

• Ownership de la capa analítica
• Diseñar y mantener modelos de datos analíticos en DBT, asegurando la trazabilidad, eficiencia y calidad de los datasets.
• Construir y optimizar consultas complejas mediante SQL en BigQuery.
• Gestionar la creación de tablas, vistas, rutinas y queries programadas (scheduled queries) para asegurar la disponibilidad y calidad de los datos.
• 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.
• Participar en la definición de buenas prácticas de versionado, testing y documentación en Git.
• Contribuir a la mejora continua de la arquitectura de datos junto al Senior Data Engineer.

Analytics Engineer, dbt, BigQuery Specialist

• Design and implement end-to-end data pipelines (ingestion → transformation → quality → delivery)
• Build and maintain data components (event-driven ingestion, transformations, APIs, catalogs)
• Write SQL-first transformations and Python for orchestration/API logic
• Implement data quality in the pipeline for validation, monitoring, and alerting systems at scale
• Optimize data models for performance, cost, and reliability across datasets
• Collaborate with Junior/Intern team members

Mid Data Practitioner, Data – Analytics Engineering

• Lead the design, implementation, and long-term ownership of complex, multi-tenant data platforms
• Build robust data components using advanced Python for APIs/orchestration and expert SQL for transformation and normalization layers
• Establish and enforce global data engineering standards and quality frameworks
• Drive continuous improvements in data infrastructure, modeling, and tooling
• Mentor junior team members and define best practices for tooling and data craft

Senior Data Practitioner – Data, Analytics Engineering

• 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

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