Data Engineer

at  Tower Research Capital

Data Engineer
Tower Research Capital, a high-frequency proprietary trading firm founded in 1998, seeks a Data Engineer to join our Data Services Team in Singapore or Hong Kong.
As a Data Engineer, you will be responsible for designing, implementing, and maintaining the infrastructure necessary to support the firm’s data management processes. You will work closely with the Data Services Team, Portfolio Management Teams, and other IT teams to ensure the reliability, security, and scalability of our data systems.

QUALIFICATIONS

• Bachelor’s degree in Computer Science, Information Technology, or a related field, or equivalent work experience
• Familiarity with ETL processes, data modeling, and database design principles
• Familiarity with database systems such as SQL Server, PostgreSQL, or MySQL
• Familiarity with database backup and recovery procedures, database security, and disaster recovery planning
• Familiarity with data replication, VLDB design, query optimization, and performance troubleshooting
• Familiarity with block, protocol (NFS / SMB) file systems
• Familiarity with object storage
• Familiarity with Airflow DAG
• At least 2 years of experience in Python and ETL related libraries such as pyodbc, SQLAlchemy, and Django
• Excellent problem-solving and troubleshooting skills
• Strong communication and collaboration skills, with the ability to work effectively as part of a team

• Designing, implementing, and maintaining the infrastructure necessary to support the firm’s data management processes, including storage, compute, and networking resources
• Troubleshooting and resolving infrastructure-related issues, including hardware and software failures, network connectivity issues, and security vulnerabilities
• Automating infrastructure deployment and maintenance processes to improve efficiency and reduce the risk of human error
• Designing, building, and maintaining the data pipelines and systems necessary to support the firm’s fundamental data analysis and trading strategies
• Developing and implementing data quality checks and monitoring processes to ensure the integrity of our data
• Collaborating with data scientists, portfolio managers, and traders to understand their data requirements and developing solutions to meet their needs
• Building and maintaining databases, warehouses, and data lake to support reporting, analysis, and research
• Implementing ETL processes to extract data from various sources and transforming it into a structured format suitable for analysis
• Developing and maintaining database backup and recovery procedures to ensure the availability of critical data
• Monitoring database performance and tuning database parameters to optimize system performance
• Providing technical support for database-related issues, including troubleshooting and problem resolution
• Developing and maintaining Airflow workflows to support our data pipelines, ensuring that they are reliable, efficient, and scalable
• Monitoring the performance of Airflow workflows and making recommendations for optimization and improvement
• Providing technical support for Airflow-related issues, including troubleshooting and problem resolution
• Staying up-to-date with the latest trends and technologies in data engineering and financial services, and recommending improvements to our systems and processes accordingly
• Participating in on-call rotation for infrastructure-related incidents