数据开发

About the company Trust Wallet is the leading non-custodial cryptocurrency wallet, trusted by over 200 million people worldwide to securely manage and grow their digital assets. Our vision is to give individuals the freedom to own their assets, confidently participate in the future economy, and access opportunities that enhance their lives. Our mission is to be a trusted personal companion — helping users safely navigate Web3, the on-chain economy, and the emerging AI-powered future. With support for over 10 million assets across 100+ blockchains, Trust Wallet offers a seamless, multi-chain experience backed by industry-leading self-custody technology, a vibrant community, and a growing ecosystem of partners. The Opportunity As a Data Engineer, you will play a key role in shaping the data infrastructure that powers our AWS-based data lake. You’ll design, deploy, and maintain data pipelines to ensure a seamless, scalable, and reliable flow of information that underpins our analytics and decision-making. By leveraging your expertise with AWS, Databricks, Airbyte (or similar ELT/ETL tools), and custom solutions in Python or Go, you’ll collaborate closely with our team to transform raw data into a strategic asset for the company. Responsibilities Data Infrastructure Setup: Architect and maintain robust, scalable, and secure data infrastructure on AWS leveraging Databricks. Data Pipeline Development: Design, develop, and maintain data pipelines, primarily using tools like Airbyte and custom-built services in Go, to automate data ingestion and ETL processes. Data Lake Management: Oversee the creation and maintenance of the data lake, ensuring efficient storage, high data quality, and effective partitioning, organization, performance, monitoring and alerting. Integration and Customization: Integrate tools like Airbyte with various data sources and customize data flows to align with specific business needs. Where necessary, build custom connectors in Go to support unique data requirements. Performance and Scalability: Optimize data pipelines and data lake storage for performance and scalability, ensuring low latency and high availability. Data Governance and Security: Implement best practices for data governance, security, and compliance in AWS and Databricks, including access control, encryption, and monitoring. Collaboration and Documentation: Work closely with platform engineers, data analysts and other stakeholders to understand data requirements and document infrastructure, processes, and best practices.