Director Of Data Engineering

Overview

• Lead data engineering initiatives to support investment, advisory, and operational decision-making.
• Collaborate with portfolio managers, advisors, and operations teams to deliver analytical capabilities.
• Utilize a modern data stack including Snowflake, dbt, Airflow, and Fivetran.
• Develop high-quality datasets and delivery mechanisms for descriptive and predictive analytics.
• Evaluate and implement tools, APIs, and BI platforms for data accessibility.
• Manage a small team of data engineers and set technical direction for data delivery.
• Drive migration from legacy data stores to Snowflake incrementally.
• Ensure data quality standards for AI applications and strategic recommendations.

Key Responsibilities & Duties

• Own the dbt project, ensuring architecture, testing, and documentation standards.
• Enable analytical capabilities by building required data layers.
• Evaluate build-vs-buy decisions for analytical tooling and data delivery mechanisms.
• Design and deliver analytics-ready datasets in Snowflake.
• Expose curated data via APIs, AI tools, and BI platforms.
• Manage and mentor a team of data engineers and senior DBAs.
• Partner with stakeholders to translate analytical needs into data products.
• Establish and enforce data quality standards and certification workflows.

Job Requirements

• Bachelor of Science in a relevant field.
• 7+ years of data engineering experience, including 2+ years managing engineers.
• Proficiency with Snowflake, dbt, Airflow, and Python in production environments.
• Strong SQL skills for performance optimization and complex queries.
• Experience with financial services data and layered data architecture.
• Familiarity with Linux, Git, CI/CD pipelines, and infrastructure-as-code concepts.
• Preferred experience with BI platforms and AI/ML data pipelines.
• Comfort with Azure cloud services and Terraform for resource management.