Description
Joining Razer will place you on a global mission to revolutionize the way the world games. As a Senior Data Engineer, you'll be part of a cross-function team that's responsible for the full software development life cycle, from conception to deployment. Your primary responsibilities will include designing, building, and maintaining the systems and infrastructure that enable the processing, collection, storage, and analysis of large volumes of data.
Your key responsibilities will be:
Data Pipeline Development: design, build, and maintain data pipelines to collect, process, and transform data from various sources into a usable format for analysis and reporting.
Data Integration: integrate data from different sources, including databases, APIs, and third-party services, ensuring data consistency and accuracy.
Database Management: design and manage databases, both relational (RDBMS) and non-relational (NoSQL), optimizing for performance and scalability.
Data Warehousing: develop and maintain data warehouses and data lakes, ensuring that data storage solutions are efficient and support analytical needs.
ETL Processes: implement Extract, Transform, Load (ETL) processes to move and transform data, ensuring that data is clean, accurate, and accessible for analytics.
Data Quality and Governance: monitor and maintain data quality, implementing data governance practices to ensure data integrity and compliance with standards and regulations.
Performance Optimization: optimize data processing and storage for performance and cost-efficiency, including indexing, partitioning, and query optimization.
Data Security: implement and enforce security measures to protect sensitive data from unauthorized access and breaches.
Documentation and Reporting: document data processes, architectures, and workflows, and create reports on data pipeline performance and data quality.
Improvement: continuously improving data systems and processes by optimizing pipelines, enhancing data quality, scaling infrastructure, automating workflows, and maintaining best practices
Collaboration: work with data scientists, analysts, and other stakeholders to understand data needs and provide the necessary infrastructure and support for data analysis.