Senior Analyst, Quantitative Data Science

iA Financial Group (Industrial Alliance); Toronto; Hybrid; CAD 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid work environment

Competitive benefits & pension

Stock purchase plan

Wellness/personal development days

Job summary

iA Financial Group (Industrial Alliance) seeks a Senior Quantitative Data Scientist/Engineer to design, build, and scale analytical data products for Investments. You will collaborate with Portfolio Management, Trading, Risk, and Research to deliver trusted data assets, automate pipelines, and enable AI-enabled analytics across the organization.

The role emphasizes cloud-native solutions on Google Cloud, dbt transformations, and scalable visualization with Power BI, Streamlit-like interfaces,

Qualifications

• 5+ years of relevant experience at the intersection of finance, analytics and data engineering.

Responsibilities

• Own end-to-end analytics products from ingestion to delivery.

Skills

Python

Data engineering

DBT

GCP / Google Cloud

BigQuery

Airflow / Prefect / Dagster

Power BI

AI / analytics workflows

GitHub / CI/CD

Education

Bachelor’s or Master’s degree in CS/Engineering/Finance

Tools

dbt

BigQuery

Docker

GitHub

Prefect / Dagster / Airflow

Power BI

Job description

The Data Science function within iA Global Asset Management (iAGAM) is a key driver of strategic transformation across Investments, contributing to the organization’s long-term vision and scalable systems and analytics objectives. The team works closely with Front Office investment teams to modernize analytical workflows, enable cloud-native solutions, and accelerate the adoption of advanced analytics and AI capabilities.

Within this mandate, Core Analytics focuses on delivering trusted analytical data products, scalable analytics solutions, and standardized investment datasets that enable consistent decision-making across investment teams. The team helps modernize the data and analytics foundation that powers reporting, quantitative analysis, portfolio insights, and AI-enabled investment workflows.

Role Overview

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.

As a Quantitative Data Engineer, 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.

You will 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.

This is a hands‑on role for someone who enjoys building high‑quality data and analytics solutions, working close to investment decision‑making, and translating financial workflows into scalable analytical products. While the role requires credible financial and quantitative literacy, it is not intended to be a Front Office quant research role.

What you’ll accomplish with us

Investment Data Products & Analytics

Partner with investment teams such as Portfolio Management, Asset Allocation, Trading, Performance, Risk, Research, and other investment groups to:

• 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.

End‑to‑End Analytics Product Ownership

• 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.

Visualization & Business Enablement

• 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.

Data Engineering & Platform Contributions

• 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.

Examples of Work You May Contribute To

• Performance and attribution analytics platforms.
• Portfolio holdings, positions, exposure, and time‑series data products.
• Standardized investment datasets used across multiple teams.
• Standardized dbt transformations and curated data marts supporting performance, attribution, holdings, positions, and market data domains.
• Modernized Power BI reporting solutions and semantic models.
• Streamlit‑based analytical applications for investment users.
• Data quality monitoring, validation, and reconciliation frameworks.
• Reusable pipeline and dbt model templates for ingestion, transformation, validation, scheduling, and monitoring.
• Development of reusable dbt models, data marts, tests, documentation, lineage, and semantic‑layer components.
• AI‑ready analytical datasets and semantic layers.
• Data products supporting research, reporting, forecasting, portfolio analytics, and investment insights.

What could accelerate your success in this role

We’re looking for someone who:

• 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.

Education & Experience

• 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.

Why you’ll love working with us?

• A work environment where learning and development merge with a collective pursuit of excellence;
• A healthy, safe, fair, and inclusive environment where potential can be freely expressed and developed;
• The opportunity to work in a hybrid environment, supported by flexibility and access to inspiring and innovative workspaces;
• Competitive 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, and much more!

The typical hiring range for this position is between 70,000$ and 110,000$ CAD per year; the base salary offered may vary depending on knowledge, skills, years of experience, and internal equity related to the role. At iA, we are committed to offering a fair, equitable, and market‑based compensation structure. Our market data is updated annually to reflect the most current market conditions.