Senior Data Engineer - Asian Timezones

Company Overview hermeneutic research is a best-in-class proprietary trading firm. It deploys research-driven discretionary and systematic strategies as well as makes strategic long-term investments. The partners' decade-long history of success in trading and business building and a firm-wide cultural emphasis on alpha generation, open debate, relentless iteration, and teamwork are key to the firm's continued expansion in a challenging market environment that has hamstrung competitors. A hard-wired emphasis on risk management and opportunistic market participation ensures that hermeneutic research will continue its growth trajectory in the coming decades. Job Overview We are looking for a Senior Data Engineer to help us architecting, implementing and operating the complete data infrastructure pipeline for our Research and Trading operations. This role will be crucial in building a scalable, reliable, and cost-efficient system for handling vast amounts of market trading data, real-time news feeds and a variety of internal and external data sources. The ideal candidate will be a hands-on professional who understands the entire data lifecycle and can drive innovation while collaborating across research and engineering teams to meet their needs. Key Responsibilities Design, build, and optimize scalable pipelines for ingesting, transforming, and integrating large-volume datasets (market data, news feeds and various unstructured data sources). Ensure data quality, consistency, and real-time monitoring using tools like DBT, 3rd party libraries that can facilitate data validation processes. Develop processes to normalize and organize our data warehouse for use across different departments. Apply advanced data management practices to ensure the scalability, availability, and efficiency of data storage. Ensure the infrastructure supports trading and research needs while maintaining data integrity, security, and performance at scale. Collaborate with research and analytics teams to understand their data needs and build frameworks that empower data exploration, analysis, and model development. Create tools for overlaying data from multiple sources. Ensure that data storage, processing, and management are done in a cost-effective manner, optimizing both hardware and software resources. Implement solutions that balance high performance with cost control. Stay ahead of the curve by continuously evaluating and adopting the most suitable technologies for the organization’s data engineering needs. Ensure that company’s systems align with the latest best practices in data management.