Data Engineer (Remote, US Time Zone)

We are looking for a Data Engineer to play a key role in shaping and scaling our platform as the organization grows in complexity and data maturity.

In this role, you will be responsible for designing and evolving the core data infrastructure that powers analytics, machine learning, and critical business decisions. You will take ownership of how data flows across the company, from ingestion to consumption, ensuring it is reliable, well-modeled, and accessible to a wide range of stakeholders.

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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