• Pipeline Development and Optimization: Build and maintain reliable, scalable ETL/ELT pipelines using modern tools and best practices, ensuring efficient data flow for analytics and insights. • Data Modeling and Transformation: Design and implement effective data models that support business needs, enabling high-quality reporting and downstream analytics. • Collaboration Across Teams: Work closely with data analysts, product managers, and other engineers to understand data requirements and deliver solutions that meet the needs of the business. • Ensuring Data Quality: Develop and apply data quality checks, validation frameworks, and monitoring to ensure the consistency, accuracy, and reliability of data. • Performance and Efficiency: Identify and address performance issues in pipelines, queries, and data storage. Suggest and implement optimizations that enhance speed and reliability. • Security and Compliance: Follow data security best practices and ensure pipelines are built to meet data privacy and compliance standards. • Innovation and Continuous Improvement: Test new tools and approaches by building Proof of Concepts (PoCs) and conducting performance benchmarks to find the best solutions. • Automation and CI/CD Practices: Contribute to the development of robust CI/CD pipelines (GitLab CI or similar) for data workflows, supporting automated testing and deployment.

• Design and improve the cloud structure (AWS, Azure, or GCP) of our data platform, ensuring it works well for many clients at the same time. • Build a system ready for failures, capable of recovering automatically and keeping things running if there are issues in production. • Create clear APIs, templates, and common tools so internal developers can write code faster and with fewer mistakes. • Monitor and optimize the use of cloud servers and tools (like Kubernetes) to keep the product cost-effective to run. • Identify parts of the system that have become outdated and plan how to improve them step-by-step without stopping daily work. • Review team designs and define the company's technical standards, helping to resolve questions without becoming a bottleneck. • Ensure the platform's structure inherently protects customer data and complies with security rules from the very beginning. • Decide how we measure platform performance so that if something fails, we know exactly what happened within a few minutes. • Build quick prototypes and test new tools before deciding whether to adopt them company-wide.

• Definir la estrategia analítica y el modelo funcional de la solución, traduciendo objetivos de negocio en decisiones de arquitectura. • Diseñar y construir el pipeline de ingesta y transformación sobre AWS: funciones Lambda (integradas con Microsoft Graph API), Data Lake en S3, jobs de Glue y tablas externas en Athena. • Configurar la seguridad de la infraestructura (IAM) y, según corresponda, su capa de red, asegurando que la arquitectura cumpla los estándares exigidos en entornos regulados como el financiero. • Diseñar el modelo dimensional (star schema) pensado para escalar a nuevas entidades sin rediseño, y el modelo de seguridad a nivel de datos (RLS), incluyendo su mapeo a la identidad corporativa del cliente. • Definir los estándares de arquitectura, métricas de negocio y consultas analíticas que el equipo de desarrollo ejecuta. • Actuar como autoridad técnica frente al cliente: validar la calidad de los entregables del equipo y ser el punto de contacto para decisiones de arquitectura e infraestructura. • Mantener la documentación de arquitectura como base de gobierno y transferencia de conocimiento.

Role Description This is the contact point for the local team to work closely with the headquarter teams. We are seeking an experienced Senior Data Engineer to design, implement, and maintain our data infrastructure and pipelines. The ideal candidate will have a strong background in data engineering, big data technologies, and cloud platforms. You will work closely with data scientists, analysts, and other stakeholders to ensure efficient and reliable data processing and storage solutions and who has mind set things to done. - Design, develop, and maintain scalable data pipelines and ETL processes - Implement and optimize data storage solutions, including data warehouses and data lakes - Collaborate with data scientists and analysts to understand data requirements and provide efficient data access - Ensure data quality, consistency, and reliability across all data systems - Develop and maintain data models and schemas - Implement data security and access control measures - Optimize query performance and data retrieval processes - Evaluate and integrate new data technologies and tools - Mentor junior data engineers and provide technical leadership - Collaborate with cross-functional teams to support data-driven decision-making Qualifications - Bachelor's or master's degree in computer science, Engineering, or a related field - 7+ years of experience in data engineering or related roles - Strong programming skills in Python, Java, or Scala - Extensive experience with big data technologies such as Hadoop, Spark, and Hive - Proficiency in SQL and experience with both relational and NoSQL databases - Experience with cloud platforms (AWS, Azure,) and their data services - Knowledge of data modeling, data warehousing, and ETL best practices - Strong problem-solving skills and attention to detail - Excellent communication and collaboration skills Requirements - Experience with stream processing technologies (e.g., Kafka, Flink or delta live table) - Familiarity with data governance and compliance requirements - Experience with containerization and orchestration tools (e.g., Docker, Kubernetes) - Contributions to open-source projects or relevant certifications - Experience in Tencent big data platform - Experience in PowerBI will be preferable Benefits - 16~20 days of fully-paid annual leaves - Full social insurance and housing fund - Year-end bonus - WLB - No business trip - Best Culture - Clear focus - Diverse Workplace (Our members are from around the world!) - Non-hierarchical and agile environment - Growth opportunity and career path