Software Engineering, Senior Data Engineer

About the role

In conjunction with the Investment Data Management Office, the Senior Investment Data Engineer contributes to a strategic initiative to unify and harmonize investment data across the organization. This initiative supports enhanced investment decision making, risk management, analytics, and client reporting for a multi-asset investment platform by delivering consistent, timely, accurate, and user-friendly data to investment professionals, risk teams, and clients.

Responsibilities

• Develop and maintain data models in dbt (Data Build Tool) within Snowflake, implementing business logic and adhering to established data standards and architectural principles.
• Contribute to dbt projects by building modular, scalable, and well-documented transformation pipelines.
• Design, develop, and support scalable data pipelines and data warehouse solutions for reporting, analytics, and operational use cases.
• Participate in development activities and support implementation of solutions aligned with business and program objectives.
• Assist in improving data quality, resiliency, controls, monitoring, and operational efficiency across the investment data platform.
• Troubleshoot data and system issues, identify root causes, and support resolution efforts.
• Partner with senior engineers, platform leads, architects, and business stakeholders to support delivery of investment data solutions.
• Support integration of data ingestion, transformation, orchestration, and validation tools within the unified data platform.
• Contribute to enhancement of data engineering and analytical engineering capabilities across the platform.
• Provide operational and production support, including assistance during unexpected outages or incidents.

Requirements

• Bachelor’s degree in Computer Science or related discipline.
• 3–5+ years of experience designing, developing, and supporting data-oriented applications and platforms.
• 2+ years of hands-on experience with SQL and advanced SQL concepts.
• Experience developing and maintaining data models using dbt (Data Build Tool).
• Experience working with data integration (ETL/ELT), data warehousing, and analytics architectures, with understanding of data engineering best practices and design principles.
• Experience with Snowflake and/or other cloud-native databases is preferred.
• Development experience with cloud-based PaaS platforms such as Microsoft Azure, Google GCP, or Amazon AWS.
• Familiarity with Agile SDLC, DevOps practices, CI/CD processes, and cloud technologies.
• Understanding of modern data architecture concepts such as unified data management, data mesh, event-driven architecture, real-time data flows, and data virtualization is a plus.