Director Of Data Engineering

Director of Data Engineering Overview

• The Director of Data Engineering will lead data initiatives, empowering analytical capabilities for investment, advisory, and operational decision-making.
• Collaborate with portfolio managers, advisors, and operations teams to deliver impactful data-driven solutions.
• Manage a modern data stack including Snowflake, dbt, Airflow, and Fivetran to ensure optimal performance.
• Develop high-quality, business-ready datasets and implement tools for predictive analytics.
• Lead a small team, setting technical direction and fostering professional growth.
• Drive migration from legacy systems to Snowflake, ensuring seamless integration and data quality.
• Establish standards for data certification to support AI applications and strategic recommendations.
• Contribute to a collaborative and entrepreneurial work environment with flexible hybrid work arrangements.

Director of Data Engineering Key Responsibilities & Duties

• Own and maintain the dbt project, ensuring robust architecture, testing, and documentation.
• Enable analytical capabilities by building essential data layers for investment and advisory teams.
• Evaluate and implement BI platforms and data delivery mechanisms for optimal outcomes.
• Develop and deliver analytics-ready datasets in Snowflake, ensuring high data quality.
• Design and manage Airflow DAGs for pipeline orchestration and environment parity.
• Mentor and manage a team of data engineers, fostering professional development.
• Partner with stakeholders to translate analytical needs into actionable data products.
• Implement data quality standards to support AI applications and strategic analytics.

Director of Data Engineering Job Requirements

• Bachelor of Science degree in a relevant field is required.
• At least 7 years of data engineering experience, including 2 years in management roles.
• Proficiency in Snowflake, dbt, and Airflow for production environments.
• Strong SQL skills with expertise in performance optimization and complex queries.
• Experience with financial services data, including securities identifiers and portfolio structures.
• Hands-on Python experience for data automation and scripting.
• Familiarity with Linux, Git, CI/CD pipelines, and infrastructure-as-code concepts.
• Preferred experience with BI platforms, CRM data, and AI/ML pipelines.