Senior Data Engineer with experience working within the investment industry for ETL data integration and development for an Investment Data Solution

Our client is seeking a Senior Data Engineer with experience working within the investment industry for ETL data integration and development for an Investment Data Solution

Location: Remote within Canada

Position Overview: We are seeking a skilled Data Engineer to collaborate with teams of analysts and system architects to deliver robust, scalable data solutions for investment data. The ideal candidate will have a strong background in data engineering, ETL development, database architecture, and a deep understanding of both relational and non-relational databases. Additionally, they will work on building data pipelines, ensuring data accuracy, and supporting analytics tasks across multiple investment-related platforms.

Must-Haves:

• 3+ years of experience in data engineering
• 7+ years of in developing ETL processes
• 3+ years of in designing and optimizing database structures for investment data, with knowledge of big data technologies such as data lakes and warehouses.
• 5+ years of hands-on experience with Python and SQL programming languages.
• 3+ years of experience with Azure data tools such as Azure Data Lake, Azure Data Factory, and Databricks (DBT is a plus).
• 2+ years of experience working in an Agile environment, using DevOps tools like JIRA and Azure DevOps (ADO).

Nice-to-Haves:

• Industry experience with investment companies
• Collaborating with data analysts and investment professionals to develop data pipelines and workflows supporting portfolio analysis, risk assessment, and performance attribution.
• Investment analytics concepts such as portfolio analysis, risk assessment, and performance attribution.
• Investment analytics, including working with market data feeds, trading platforms, and financial databases.

Responsibilities:

• Collaborate with client teams, data analysts, and system architects to gather data requirements and design scalable data models, pipelines, and integration processes for investment-related data.
• Develop and optimize ETL processes to gather and transform data from various sources such as market data providers, trading platforms, and internal systems.
• Perform data analysis, validation, and cleansing to ensure data accuracy and integrity.
• Design and optimize database structures tailored for investment data storage and retrieval, including both relational and dimensional data modeling.
• Implement modern big data technologies, including data lakes and data warehouses, to enhance data storage and management.
• Work with Python and SQL to develop data workflows and processes.
• Utilize Azure technologies such as Azure Data Lake, Azure Data Factory, and Databricks to build and maintain data pipelines.
• Participate in Agile software development using tools such as JIRA and Azure DevOps.
• Collaborate with investment professionals to support analytics models and risk assessments for portfolio analysis.