SR Data Engineer with investment experience to develop and manage data solutions
S.i. Systèmes Toronto
Our client is seeking a SR Data Engineer with investment experience to develop and manage data solutions Job Summary: As a Data Development Engineer, you will lead the development and management of data solutions specific to the investment domain.
You will work closely with investment professionals, data analysts, and other stakeholders to design and implement data systems that support investment decision-making processes. This role will establish and grow a Data Development Center of Excellence, fostering a culture of innovation and implementing best practices in data development.
Strong technical skills in data engineering, investment data analysis, and financial domain knowledge are required. Responsibilities: Investment Data Solution Design: Collaborate with investment teams and data analysts to understand data requirements and translate them into scalable and robust data solutions.
Design data models, pipelines, and integration processes to acquire, transform, and store investment-related data. Investment Data Integration and Analysis: Develop ETL processes and workflows to gather and combine data from multiple sources, such as market data providers, trading platforms, and internal systems.
Perform data validation, cleansing, and enrichment to ensure accuracy and integrity. Develop tools for data visualization and reporting. Database Management: Design and optimize database structures and schemas for investment data storage and retrieval.
Ensure efficient indexing, partitioning, and query optimization to support fast and accurate data retrieval. Monitor database performance and recommend improvements. Investment Analytics: Work with data analysts and investment professionals to implement investment analytics models, algorithms, and techniques.
Develop data pipelines to support portfolio analysis, risk assessment, performance attribution, and other investment-related analytics tasks. Data Quality and Governance: Establish data quality standards and governance policies specific to investment data.
Implement validation, cleansing, and enrichment processes to maintain high-quality investment data. Ensure compliance with regulatory requirements and industry best practices. Must-Have Qualifications: 5+ years in data engineering, database development, or a similar role, preferably within the investment industry.
Python, SQL , and R experience Big data technologies, including data lakes and data warehouses Understanding of investment data sources such as market data feeds, trading platforms, and financial databases. Hands-on experience with Azure Data Lake, Azure Data Factory, Databricks, and DBT .
Nice-to-Have Qualifications: Experience with Eagle Pace, Aladdin, Simcorp and or other investment tools Experience with ETL tools, data integration platforms, and data visualization tools. Familiarity with portfolio analysis, risk assessment, and performance attribution.
Proven experience working with cloud-based data platforms. Strong understanding of relational and non-relational databases, data modeling, and query optimization. Knowledge of investment regulations, compliance requirements, and data privacy regulations.
You will work closely with investment professionals, data analysts, and other stakeholders to design and implement data systems that support investment decision-making processes. This role will establish and grow a Data Development Center of Excellence, fostering a culture of innovation and implementing best practices in data development.
Strong technical skills in data engineering, investment data analysis, and financial domain knowledge are required. Responsibilities: Investment Data Solution Design: Collaborate with investment teams and data analysts to understand data requirements and translate them into scalable and robust data solutions.
Design data models, pipelines, and integration processes to acquire, transform, and store investment-related data. Investment Data Integration and Analysis: Develop ETL processes and workflows to gather and combine data from multiple sources, such as market data providers, trading platforms, and internal systems.
Perform data validation, cleansing, and enrichment to ensure accuracy and integrity. Develop tools for data visualization and reporting. Database Management: Design and optimize database structures and schemas for investment data storage and retrieval.
Ensure efficient indexing, partitioning, and query optimization to support fast and accurate data retrieval. Monitor database performance and recommend improvements. Investment Analytics: Work with data analysts and investment professionals to implement investment analytics models, algorithms, and techniques.
Develop data pipelines to support portfolio analysis, risk assessment, performance attribution, and other investment-related analytics tasks. Data Quality and Governance: Establish data quality standards and governance policies specific to investment data.
Implement validation, cleansing, and enrichment processes to maintain high-quality investment data. Ensure compliance with regulatory requirements and industry best practices. Must-Have Qualifications: 5+ years in data engineering, database development, or a similar role, preferably within the investment industry.
Python, SQL , and R experience Big data technologies, including data lakes and data warehouses Understanding of investment data sources such as market data feeds, trading platforms, and financial databases. Hands-on experience with Azure Data Lake, Azure Data Factory, Databricks, and DBT .
Nice-to-Have Qualifications: Experience with Eagle Pace, Aladdin, Simcorp and or other investment tools Experience with ETL tools, data integration platforms, and data visualization tools. Familiarity with portfolio analysis, risk assessment, and performance attribution.
Proven experience working with cloud-based data platforms. Strong understanding of relational and non-relational databases, data modeling, and query optimization. Knowledge of investment regulations, compliance requirements, and data privacy regulations.
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