Lead Data Engineer

Lead Data Engineer | Data Integration, Advanced SQL, DBT

Our client, a well-respected financial services firm, has an immediate need for a Lead Data Engineer onsite Tuesday, Wednesday, Thursday (3 days per week). Candidates will be expected to interview for this position onsite and will need to be local to Boston. 

This is a mid- level role that requires expertise in working in data integration. The ideal candidate has 5+ years of experience and is local to Boston. Position must-haves include: advanced SQL skills, strong dbt, cloud data warehouse, and modern ELT/ETL experience. Snowflake and asset management experience are highly preferred. This role is posted internally for full-time, but the manager needs someone as soon as possible. He is open to considering contract-to-hire if all goes well.

THE ROLE
In conjunction with the Investment Data Management Office, the Lead Data Engineer contributes to a long-term strategic initiative to unify and harmonize our investment data. This initiative enables enhanced investment decision-making, risk management, and client reporting for our multi-asset platform by delivering consistent, timely, accurate, and user-friendly data to investors, risk teams, and clients.
  
The Investment Data Management Office is actively searching for a Lead Data Engineer to implement data engineering and analytics solutions. Primary responsibilities include full implementation and maintenance of data ingestion, data maintenance, data validation, and data delivery of investment data. We are looking for someone who thrives in an agile, collaborative, team-based environment, working closely with technology peers across the organization, investment professionals, and key vendor partners. This position offers the opportunity to shape the future of investment data at our organization.

WHAT YOU WILL DO

• Develop and maintain data models in dbt (Data Build Tool) within Snowflake, implementing business logic and ensuring alignment with existing architecture and data standards.
• Manage and contribute to dbt projects, ensuring code quality, proper documentation, and alignment with modular, scalable design patterns.
• Design, build, and administer scalable data pipelines and a robust data warehouse to support reporting, analytics, and operational use cases.
• Lead and participate in all development activities; develop and implement solutions to meet business requirements that align with program strategic objectives 
• Drive continuous improvement of data quality, resiliency, control, efficiency, and monitoring
• Troubleshoot complex system interactions to find the root cause of problems
• Partner with platform lead to design, develop, implement, and deploy new software components to investment data platform
• Partner with data architect to evaluate and finalize the unified data model
• Partner with the integration architect to upgrade and integrate data ingestion and data delivery tools with the unified data platform
• Upgrade and integrate transformation tools, data validation tools, and orchestration tools with the unified data platform to implement data engineering, analytical engineering, and data maintenance capabilities.
• Provide support during unexpected outages

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

• Experience working within the asset management industry and investment data domain, with exposure to multi-asset investment 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.
• Demonstrated interest in emerging AI technologies and an understanding of how AI-driven tools can improve day-to-day engineering processes, data quality, operational efficiency, and analytics workflows.
• Familiarity with leveraging AI-assisted development, automation, or data engineering best practices to enhance productivity and continuous improvement initiatives is a plus.