Data Engineer - Wealth Technology

Dynasty Financial Partners•St Petersburg, FL

27d•Onsite

About The Position

Dynasty Financial Partners is seeking a motivated, self-starting individual to join our team and pursue our shared goal of providing exceptional service for our clients. As a Data Engineer, you will serve as a dedicated resource to support our Data Lake and assist on internal and external client solutions/product-based support. You will identify, develop, and implement product and technology-based solutions to increase operational efficiency, improve accuracy, and support platform adoption. Please Note: This job is an IN-OFFICE position located in St. Petersburg, FL. If you are unable or unwilling to fulfill this requirement of the position, please refrain from applying

Requirements

Bachelor's degree in computer science, Engineering, or related field; master's degree is a plus.
Minimum of 3 years of experience as a Data Engineer with a focus on Azure cloud services.
Strong experience with Azure Databricks for data processing and analytics.
Proficient in Azure Data Factory for creating, scheduling, and orchestrating data workflows.
Familiarity with data modeling, SQL, and data warehousing principles.
Experience with programming languages such as Python, Scala, or SQL for data manipulation and transformation.
Solid understanding of CI/CD practices and version control systems (e.g., Git).
Excellent problem-solving skills, attention to detail, and the ability to work in a fast-paced environment.
Strong communication and collaboration skills to work effectively with both technical and non-technical team members.
Azure Data Engineer Associate
Azure Data Fundamentals
Microsoft PowerBI

Nice To Haves

Knowledge of Delta Lake and Lakehouse architecture patterns
Experience with Databricks Unity Catalog for data governance and metadata management
Work history in the Wealth Management industry is a plus

Responsibilities

Design, develop, and maintain reliable and efficient data pipelines using Azure Data Factory and Azure Databricks.
Construct and maintain complex data processing systems, ensuring they are scalable, repeatable, and secure.
Develop data pipelines that answer critical business questions and solve internal and external reporting needs.
Develop and maintain data models and data architecture to support data lakehouse architecture.
Implement automated data validation, testing, and error handling mechanisms within data pipelines.
Stay up to date with the latest Azure services and technologies, as well as industry best practices, to continuously improve the data engineering processes.
Document all data engineering processes, systems, and designs to ensure knowledge sharing and business continuity.