Lead Data Engineer

Marchon Partners · Boston, MA · 1 mo ago

Hybrid$67–$77/hrOther

The Role

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.

Primary Responsibilities

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.
Troubleshoot complex system interactions to find the root cause to 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 integration architect to upgrade and integrate data ingestion and data delivery tools with the unified data platform.
Upgrade and integrate transformation tool, data validation tool and orchestration tools with the unified data platform to implement data engineering, analytical engineering and data maintenance capabilities.
Provide support during unexpected outages.

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.

Troubleshoot complex system interactions to find the root cause to 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 integration architect to upgrade and integrate data ingestion and data delivery tools with the unified data platform.

Upgrade and integrate transformation tool, data validation tool and orchestration tools with the unified data platform to implement data engineering, analytical engineering and data maintenance capabilities.

Provide support during unexpected outages.

Qualifications

Bachelor's degree in Computer Science or related disciplines.
5-6+ years of experience in design, development 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 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.

PREFERRED QUALIFICATIONS

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.