Data Engineer (Remote, US Eastern Time Zone)

Key Responsibilities

• Design, build, and maintain production-grade data pipelines and data-intensive systems
• Configure, schedule, and monitor data pipeline execution to ensure reliability , maintainability , and timely delivery across all data processes
• Deploy and manage data infrastructure on AWS or on-premises , ensuring scalability, security, and cost-efficiency
• Monitor and optimize relational and NoSQL database performance (e.g., MySQL, PostgreSQL, MongoDB) to ensure efficient querying, indexing, and data access at scale
• Ensure reliable ingestion, transformation, and availability of large-scale and time-series financial data , considering financial-specific data quality characteristics.
• Implement and maintain data quality, validation, and monitoring mechanisms across data workflows
• Define and evolve data models and storage structures across relational and NoSQL systems
• Collaborate with Data Scientists and Analysts to ensure accurate and efficient data access for analytics and modeling use cases
• Optimize data processing workflows for performance, reliability, and operational stability
• Troubleshoot complex data issues and drive root cause analysis.
• Contribute to technical decision-making and help define the roadmap for data infrastructure.
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Requirements

• Degree in Computer Science, Engineering, or a related field.
• Strong programming skills in Java or Python (or other JVM-based languages)
• Strong understanding of data modeling, SQL, and NoSQL databases
• Strong focus on data quality, validation, and monitoring practices
• Experience working with large-scale and time-series datasets in financial contexts, with an understanding of their structure and common data quality challenges
• Experience building and maintaining production data pipelines or data-intensive systems , with focus on reliability and performance
• Ability to troubleshoot and optimize production data systems
• Experience with workflow orchestration and data pipeline scheduling tools
• Solid understanding of financial data structures and concepts, with the ability to model and validate financial data accurately
• Strong problem-solving skills and attention to detail.
• Professional proficiency in English.
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Bonus Points