Role Description Azumo is currently looking for a highly motivated Big Data Engineer to develop and enhance data and analytics infrastructure. The position is FULLY REMOTE based in Latin America. This role will focus on building scalable, reliable, and governed data systems that power analytics, live operations, player insights, publishing intelligence, and business decision-making across Riot’s gaming ecosystem. The ideal candidate has strong hands-on experience with Databricks, AWS, scalable ETL pipelines, data governance, and modern analytics engineering practices. Experience supporting gaming, telemetry, live operations, or high-scale analytics environments is strongly preferred. The Data Engineer will be based remotely. Compensation commensurate with experience and candidate potential. Responsibilities - Design, build, and maintain scalable ETL/ELT pipelines using Databricks and AWS - Improve ingestion pipeline quality, reliability, scalability, and governance - Develop and optimize core data models and foundational data tables - Build analytics-ready datasets to support player insights, publishing analytics, esports analytics, and operational reporting - Implement data governance, data quality, lineage, and observability practices - Collaborate with product, analytics, engineering, and business stakeholders to support data-driven decision-making - Optimize large-scale data processing workflows for performance and cost efficiency - Support centralized player data models, viewer analytics, publishing activity systems, and operational metrics - Contribute to the unification of fragmented data ecosystems across multiple game teams and organizations - Build and maintain reliable orchestration workflows and scheduling systems - Participate in architectural discussions around scalability, governance, and data platform modernization Qualifications - Strong experience with Databricks - Strong SQL and Python programming experience - Experience building scalable ETL/ELT pipelines - Experience with AWS data ecosystem and cloud-native architectures - Experience with dbt, Apache Airflow or similar orchestration tools - Strong understanding of data modeling and analytics engineering - Experience working with large-scale structured and semi-structured datasets - Experience implementing data governance and data quality practices - Experience optimizing pipeline performance and scalability - Strong communication and collaboration skills - Ability to work independently within distributed engineering teams Nice to Have - Gaming industry experience - LiveOps or telemetry pipeline experience - Experience supporting analytics for player behavior, engagement, monetization, or publishing systems - Experience with real-time or streaming data systems - Experience with viewer analytics or esports data ecosystems - Experience with KPI reporting and operational dashboards - Familiarity with cost optimization and cloud infrastructure analytics - Experience working in fast-paced product engineering organizations Basic Qualifications - BS or Master’s degree in Computer Science, related degree, or equivalent experience - 5+ years experience with data-related and data management responsibilities - Deep expertise in designing and building data warehouses and big data analytics systems - Practical experience manipulating, analyzing and visualizing data - Self-driven and motivated, with a strong work ethic and a passion for problem solving - Professional English proficiency (B2/C1) Benefits - Paid Time Off - Mentored Career Development - U.S. Holidays - USD Remuneration - Profit Sharing - Maternity Coverage - 100% Remote

- **Desenvolvimento de Pipelines:** Criar e manter pipelines de dados escaláveis e eficientes na Azure Cloud. - **Processamento de Dados:** Trabalhar com Databricks para processamento distribuído e análise de dados. - **Automação em Linux:** Administrar e automatizar rotinas em Linux, com foco em performance e segurança. - **Colaboração Interdisciplinar:** Colaborar com equipes de desenvolvimento e infraestrutura para garantir boas práticas de DataOps.

• Own the product strategy for the Data Platform, defining the vision, principles, roadmap, and prioritization framework aligned with business and engineering goals • Partner closely with the Head of Data Platform and engineering teams to ensure fast, scalable, and consistent product delivery • Act as the voice of internal users by mapping end-to-end journeys - from discoverability and onboarding to adoption, operations, and scaling - identifying friction points and opportunities for improvement • Drive product discovery and execution in collaboration with cross-functional stakeholders across Data Platform Engineering, Security, Governance and Analytics • Define and monitor success metrics such as adoption, reliability, developer productivity, cost efficiency, time-to-first-value, and data quality to guide prioritization and decision-making • Collaborate with engineering teams on technical trade-offs, sequencing, and solution design, helping balance scalability, usability and speed of delivery • Champion a product mindset across platform teams, promoting usability, self-service, scalability, reliability, and security-by-default principles • Improve platform adoption and enablement through documentation, feedback loops, onboarding experiences, and training initiatives • Contribute to building a modern, scalable data ecosystem leveraging Databricks, AWS and Python-based solutions

• Own the product strategy for the Data Platform, defining the vision, principles, roadmap, and prioritization framework aligned with business and engineering goals • Partner closely with the Head of Data Platform and engineering teams to ensure fast, scalable, and consistent product delivery • Act as the voice of internal users by mapping end-to-end journeys - from discoverability and onboarding to adoption, operations, and scaling - identifying friction points and opportunities for improvement • Drive product discovery and execution in collaboration with cross-functional stakeholders across Data Platform Engineering, Security, Governance and Analytics • Define and monitor success metrics such as adoption, reliability, developer productivity, cost efficiency, time-to-first-value, and data quality to guide prioritization and decision-making • Collaborate with engineering teams on technical trade-offs, sequencing, and solution design, helping balance scalability, usability and speed of delivery • Champion a product mindset across platform teams, promoting usability, self-service, scalability, reliability, and security-by-default principles • Improve platform adoption and enablement through documentation, feedback loops, onboarding experiences, and training initiatives • Contribute to building a modern, scalable data ecosystem leveraging Databricks, AWS and Python-based solutions