Data Engineer - ETF Platform

Key Responsibilities

• Drive AI-accelerated engineering practices, including daily use of tools such as GitHub Copilot, Claude, ChatGPT, and AWS Bedrock for coding, testing, documentation, and prototyping, with clear review standards for AI-generated code
• Design, build, and maintain data engineering workflows and platform services supporting model delivery and analytics ecosystems
• Develop platform components responsible for data ingestion, transformation, validation, and routing across internal and external systems
• Build Python-based services, APIs, and microservices supporting portfolio analytics, optimization workflows, and data pipelines
• Design and optimize SQL-based data processing (PostgreSQL, SQL Server, Snowflake), including complex queries, performance tuning, and large-scale ETL workflows
• Implement and support distributed, event-driven architectures, including Kafka and asynchronous processing patterns
• Develop and maintain cloud-native applications on AWS, including Lambda, S3, ECS/EKS, Step Functions, and Aurora
• Design and operate CI/CD pipelines to ensure reliable, repeatable deployments
• Ensure data quality, auditability, and observability through logging, monitoring, lineage tracking, and validation frameworks
• Collaborate closely with investment and analytics teams to translate portfolio construction, risk, and analytical requirements into scalable technical solutions
• Continuously improve platform reliability and performance through modern engineering practices, testing, and automation
• Participate in production support and on-call rotation as needed

Experience & Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field
• 3+ years of hands-on experience in software engineering or data engineering (financial services or asset management experience preferred)
• 3+ years of experience with Python, including data processing, APIs, or service-based architectures
• 3+ years of experience with SQL (PostgreSQL, SQL Server, and/or Snowflake), including ETL workflows and performance tuning
• Strong understanding of data engineering fundamentals, including data modeling, pipelines, validation, lineage, and error handling
• Experience with cloud-native development on AWS and containerized environments
• Familiarity with distributed systems and event-driven architectures, such as Kafka
• Experience with CI/CD, DevOps practices, and automated testing
• Exposure to portfolio analytics, risk models, or investment workflows is a plus