Lead Data Engineer

The Lead Data Engineer will drive data integration, dbt driven modeling in Snowflake, and end-to-end data pipelines for investment data. This on-site role is based in Boston, with presence required three days per week to support the Investment Data Management Office.

Responsibilities

• Create and maintain dbt based data models in Snowflake, encoding business logic while aligning with existing architecture and data standards.
• Collaborate on and steer dbt project work, emphasizing code quality, documentation, and modular, scalable design patterns.
• Architect, build, and administer scalable data pipelines and a robust data warehouse to support reporting, analytics, and operational use cases.
• Lead and contribute to development efforts, delivering solutions that meet business requirements aligned with program objectives.
• Advance data quality, resiliency, governance, efficiency, and monitoring through continuous improvement.
• Diagnose and resolve complex system interactions to identify root causes of problems.
• Collaborate with the platform lead to design, develop, implement, and deploy new software components for the investment data platform.
• Coordinate with the data architect to evaluate and finalize the unified data model.
• Work with the integration architect to upgrade and integrate data ingestion and data delivery tools within the unified data platform.
• Upgrade and integrate transformation tools, data validation tools, and orchestration tools to enable data engineering, analytical engineering, and data maintenance capabilities.
• Provide on-call support during unexpected outages.

Requirements

• Bachelor’s degree in Computer Science or related disciplines.
• 5-6+ years of experience designing, developing, and building data oriented complex applications.
• Minimum of 2-4 years of hands-on progression from SQL to Advanced SQL.
• Experience in developing and maintaining data models in dbt (Data Build Tool).
• Experience in data integration (ETL/ELT), data warehouse, and data analytics architecture with a solid understanding of design principles; knowledge of Snowflake and other cloud native databases is highly preferred.
• Development experience on cloud-based PAAS platforms such as Microsoft Azure, Google GCP, or Amazon AWS.
• Strong understanding of Agile SDLC, DevOps, and Cloud technologies, with exposure to multiple technologies, platforms, and processing environments.
• Knowledge of architectures and patterns such as unified data management (UDM), data mesh, event-driven architecture, real-time data flows, non-relational repositories, and data virtualization.
• Experience building solutions in the financial services domain with an understanding of financial instruments, transactions, and positions is desirable.
• Good interpersonal and communication skills with the ability to lead cross-team collaboration across internal and external stakeholders.

Technologies

• dbt (Data Build Tool)
• Snowflake
• Microsoft Azure
• Google Cloud Platform
• Amazon Web Services

What we are looking for

• Bachelor’s degree in Computer Science or related disciplines.
• 5-6+ years designing, developing, and building data oriented complex applications.
• 2-4+ years of hands-on experience from SQL to Advanced SQL.
• Experience in developing and maintaining data models in dbt (Data Build Tool).
• Experience in data integration (ETL/ELT), data warehouse, and data analytics architecture; familiarity with Snowflake and other cloud native databases is highly preferred.
• Development experience on cloud-based PAAS platforms such as Microsoft Azure, Google GCP, or Amazon AWS.
• Strong understanding of Agile SDLC, DevOps, and cloud technologies, with exposure to diverse technologies and environments.
• Knowledge of architectures and patterns including UDM, data mesh, event-driven, real-time data flows, non-relational repositories, and data virtualization.
• Experience delivering solutions in the financial services domain with an understanding of financial instruments, transactions, and positions is desired.
• Strong interpersonal and communication skills enabling cross-team collaboration with internal and external stakeholders.

Preferred Qualifications

• Experience in the asset management industry and investment data domain, with exposure to multi-asset platforms and related data ecosystems.
• Understanding of asset management concepts and knowledge of various financial instruments and products including traditional and alternative asset classes.
• Industry certifications in Snowflake, dbt, cloud data engineering platforms, data warehousing technologies, or financial markets / investment operations are highly valued.
• Interest in emerging AI technologies and how AI driven tools can enhance engineering processes, data quality, operational efficiency, and analytics workflows.
• Familiarity with using AI assisted development, automation, or data engineering best practices to boost productivity and continuous improvement efforts.