Data Quality Analyst improving policy, premium, and claims data integrity for Ledgebrook’s AI - native specialty insurance platform. Building controls, automated checks, and workflows across business and engineering teams.
IA
Senior Analyst, Quantitative Data Science
Posted 3 days ago
About the role
- Senior quantitative data scientist building cloud-native investment analytics products for iA Financial Group. Developing pipelines, Power BI solutions, and AI-ready datasets for investment teams.
Responsibilities
- Partner with Portfolio Management, Asset Allocation, Trading, Performance, Risk, Research, and other investment groups
- Develop and maintain analytical data products supporting investment workflows
- Translate financial and analytical requirements into scalable data solutions
- Manage performance, attribution, time-series, holdings, positions, exposures, and aggregated analytics datasets
- Ensure investment datasets are accurate, validated, timely, and well-governed
- Modernize reporting and analytical processes across Investments
- Identify opportunities to automate manual processes and improve data reliability, timeliness, and quality
- Enable trusted, reusable datasets for reporting, research, visualization, and AI initiatives
- Own analytical product lifecycles from ingestion and transformation through delivery and ongoing evolution
- Define requirements, priorities, operating expectations, and success measures with stakeholders
- Design scalable data models and transformation pipelines
- Ensure analytical products are maintainable, documented, observable, and operationally supportable
- Develop Power BI and Streamlit analytical experiences
- Develop cloud-native solutions on Google Cloud Platform using BigQuery, Cloud Storage, and dbt
- Build and maintain ETL/ELT pipelines
- Use Docker, GitHub workflows, CI/CD concepts, and Prefect, Dagster, or Airflow
- Implement data quality controls, reconciliation, monitoring, and operational runbooks
- Contribute reusable data engineering components, dbt models, standards, templates, and best practices
- Support analytics platform, semantic layer, data architecture, and AI-enabled workflow modernization
Requirements
- Strong Python development skills and experience building modern data solutions
- Strong understanding of data engineering principles, analytics engineering, data modeling, and best practices
- Experience building scalable ETL/ELT pipelines, analytical data models, and analytics engineering solutions using tools such as dbt
- Experience implementing transformation logic, testing, documentation, lineage, and reusable modeling practices using dbt or comparable analytics engineering frameworks
- Experience with Google Cloud Platform and BigQuery
- Experience with Prefect, Dagster, or Airflow
- Familiarity with GitHub, code reviews, CI/CD concepts, Docker, and modern software development practices
- Experience building Power BI solutions, semantic models, and analytical applications
- Understanding of data quality, validation, reconciliation, monitoring, and governance patterns
- Ability to diagnose issues spanning data dependencies, transformation logic, orchestration, and reporting layers
- Solid understanding of investment and financial analytics concepts
- Ability to understand investment workflows and analytical requirements
- Ability to collaborate with portfolio managers, analysts, quantitative teams, and data engineering partners
- Ability to translate business and financial requirements into scalable analytical solutions
- Experience working with financial datasets or investment analytics is highly desirable
- High ownership and accountability
- Strong collaboration skills across business, analytics, data engineering, and platform teams
- Product-oriented mindset focused on business outcomes, usability, maintainability, and reuse
- Strong problem-solving, analytical thinking, and debugging skills
- Strong communication skills with technical and non-technical audiences
- Undergraduate or master’s degree in Computer Science, Engineering, Mathematics, Finance, Financial Engineering, or a related field preferred
- 5+ years of relevant experience for intermediate candidates; 8+ years for senior candidates
- Experience working at the intersection of finance, analytics, data engineering, and technology
- Experience building data products, analytical solutions, modern reporting capabilities, or production-grade data pipelines
- Demonstrated ability to deliver and support production-grade data and analytics solutions
- Advanced proficiency in French, required for daily communication with English- and French-speaking clients and partners across Canada via email and phone calls
Benefits
- A work environment where learning and development merge with a collective pursuit of excellence
- A healthy, safe, fair, and inclusive environment
- Hybrid work environment with flexibility and access to inspiring and innovative workspaces
- Flexible group insurance
- Competitive pension plan
- Stock purchase plan
- Vacation and wellness/personal development days
- Telemedicine
- Employee and family assistance program
- Ergonomic furniture program
- Performance bonus
- Discounts on iA products
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