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About the Role

The Senior Analyst, Quantitative Data Science, plays a central role in developing and scaling analytical data products used across Investments. This role combines financial domain understanding, modern data engineering, and analytics product development to transform complex investment data into trusted, reusable, and consumable assets. You will partner directly with investment teams to understand analytical requirements, engineer scalable solutions, and deliver end-to-end products that support investment decision-making. You will work across the full lifecycle, from data sourcing and transformation through visualization, operationalization, and continuous improvement. Contribute to the modernization of the investment data ecosystem by developing cloud-native data solutions, supporting advanced visualization experiences, and helping prepare analytical assets for AI-enabled use cases. The role combines hands-on technical delivery with product ownership, business engagement, and a strong focus on reliability and long-term supportability.

Responsibilities

Develop and maintain analytical data products that support investment workflows.
Translate financial and analytical requirements into scalable data solutions.
Manage key quantitative and financial datasets, including performance, attribution, time-series, holdings, positions, exposures, and aggregated analytics.
Ensure critical investment datasets are accurate, validated, timely, and well-governed.
Support modernization of reporting and analytical processes across Investments.
Improve consistency and standardization of analytical outputs across teams.
Identify opportunities to automate manual processes and improve data reliability, timeliness, and quality.
Enable trusted, reusable datasets that support reporting, research, visualization, and AI initiatives.
Own the lifecycle of analytical products from data ingestion and transformation through delivery and ongoing evolution.
Collaborate with stakeholders to define requirements, priorities, operating expectations, and success measures.
Design scalable data models and transformation pipelines that support multiple consumers and downstream use cases.
Ensure analytical products are maintainable, well-documented, observable, and operationally supportable.
Continuously improve reliability, usability, performance, and business value of analytical products.
Apply an experimentation-driven mindset to incorporate innovation in data engineering and financial analytics delivery.
Balance short-term delivery needs with long-term sustainability, standardization, and reuse.
Develop high-impact analytical experiences using Power BI and modern application frameworks such as Streamlit.
Design intuitive interfaces that help investment teams explore, monitor, and consume analytical insights.
Support self-service analytics through standardized, trusted, and well-documented data assets.
Ensure visual outputs are accurate, validated, and aligned with governed datasets and business definitions.
Collaborate with stakeholders to improve adoption, usability, and decision support across analytical products.
Develop cloud-native analytical data solutions using Google Cloud Platform, including BigQuery, Cloud Storage, and dbt-based transformation frameworks.
Build and maintain ETL/ELT pipelines that support critical investment processes and recurring analytical workflows.
Use Docker, GitHub-based development workflows, CI/CD concepts, and orchestration frameworks such as Prefect, Dagster, or Airflow to automate and scale pipelines.
Implement data quality controls, reconciliation processes, monitoring capabilities, and operational runbooks.
Contribute reusable data engineering and analytics engineering components, dbt models, standards, templates, and best practices across Core Analytics.
Improve orchestration, observability, troubleshooting, and operational support processes.
Support modernization initiatives related to analytics platform capabilities, semantic layers, and data architecture.
Help prepare analytical datasets and products for AI-enabled workflows and future advanced analytics use cases.

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 cloud-native platforms such as Google Cloud Platform and BigQuery.
Experience with orchestration frameworks such as 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.
Familiarity with AI-enabled analytics workflows, enterprise AI capabilities, or AI-ready data product design is an asset.
Solid understanding of investment and financial analytics concepts such as: Portfolio management workflows, Performance and attribution analytics, Holdings, positions, exposures, and reference data, Market data and time-series analytics, Risk and exposure analysis, Financial reporting and compliance processes.
Ability to understand investment workflows and analytical requirements.
Ability to collaborate effectively with portfolio managers, analysts, quantitative teams, and data engineering partners.
Ability to translate business and financial requirements into scalable analytical solutions.
Ability to balance technical excellence with practical investment and operational needs.
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.
Ability to operate effectively in ambiguous environments and drive initiatives to completion.
Strong problem-solving, analytical thinking, and debugging skills.
Curiosity, continuous learning mindset, and interest in applying technology to investment data and processes.
Strong communication skills with both technical and non-technical audiences.
Ability to balance short-term delivery requirements with long-term data and platform sustainability.
Focus on quality, reliability, supportability, and continuous improvement.
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.
Experience supporting investment workflows, financial analytics, or quantitative processes is an asset.
Demonstrated ability to deliver and support production-grade data and analytics solutions.
CFA, CQF, FRM, or other quantitative or financial designation is considered an asset.
Experience working directly with Front Office or investment teams.
Prior exposure to portfolio management, trading, performance, attribution, risk, or investment reporting environments.
Experience designing analytical data products, dbt models, semantic layers, or reusable reporting datasets.
Experience supporting internal analytics platforms, shared data services, or self-service analytics ecosystems.
Familiarity with modern orchestration, containerization, automation, and observability frameworks.
Exposure to cloud-native architectures and scalable analytical application development.
Experience contributing to data governance, data quality automation, or analytical operating standards.
Experience integrating AI capabilities into analytics workflows with appropriate validation, controls, and monitoring.
Advanced proficiency in French, as the candidate will be required to communicate daily with English- and French-speaking clients and partners across Canada via email and phone calls.

Skills

Python
Data Engineering
Analytics Engineering
Data Modeling
ETL/ELT
dbt
Google Cloud Platform
BigQuery
Prefect
Dagster
Airflow
Docker
GitHub
CI/CD
Power BI
Streamlit
Data Quality
Data Validation
Data Reconciliation
Monitoring
Data Governance
AI-enabled analytics
Portfolio Management
Performance Analytics
Attribution Analytics
Market Data
Time-Series Analytics
Risk Analysis
Financial Reporting
Problem-Solving
Analytical Thinking
Debugging
Communication

Location

Quebec / 1080, Grande Allee West
Montreal / 1981 McGill College Avenue
Toronto / 26 Wellington Street East

Work Type

Hybrid

Experience Level

Senior
Intermediate

Education Level

Undergraduate or master’s degree in Computer Science, Engineering, Mathematics, Finance, Financial Engineering, or a related field

Salary/Compensations

70,000$ - 110,000$ CAD per year

Benefits

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

About the Company

iA Financial Group is the strength of a company with a human side, with its over 8,000 employees. Together, we have earned the trust of our more than four million clients and 25,000 advisors who have chosen us for their insurance, savings, and wealth management. With over $200 billion in assets and half a billion invested in technological innovation, we’re a key player in the financial services industry in Canada and the United States. The secret to our success? Investing in you, one person at a time. Because, for over 125 years, we have believed that it’s by supporting our employees and surrounding ourselves with the most reputable leaders in the industry, we will continue to innovate. At iA, we’re invested in you.

Equal Opportunity

At iA Financial Group, we support and celebrate diversity. We strive to provide a workplace that is recognized as inclusive for all, regardless of ethnic origin, nationality, language, religious beliefs, gender, sexual orientation, age, marital status, family situation, or physical or mental disability. Please note that if you need help or assistance to make the recruitment process more accessible for you, please Contact us here. Someone from our team will be happy to assist you with your needs.