• Partner with Portfolio Management, Asset Allocation, Trading, Performance, Risk, Research, and other investment teams
• 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
• Own analytical product lifecycles from data ingestion and transformation through delivery and continuous evolution
• Define requirements, priorities, operating expectations, and success measures with stakeholders
• Design scalable data models and transformation pipelines
• Develop Power BI and Streamlit analytical experiences
• Build and maintain ETL/ELT pipelines for investment processes and recurring analytical workflows
• Implement data quality controls, reconciliation, monitoring, and operational runbooks
• Contribute reusable dbt models, standards, templates, documentation, lineage, and semantic-layer components
• Support cloud-native platform modernization and AI-enabled analytical workflows
• Diagnose issues across data dependencies, transformation logic, orchestration, and reporting layers

Requirements

- Strong Python development skills
- Solid understanding of data engineering, analytics engineering, data modeling, and best practices
- Experience building scalable ETL/ELT pipelines and analytical data models using dbt or comparable frameworks
- Experience with transformation logic, testing, documentation, lineage, and reusable modeling practices
- 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
- Solid understanding of investment and financial analytics concepts
- Ability to understand investment workflows and translate business and financial requirements into scalable solutions
- Experience working with financial datasets or investment analytics is highly desirable
- 5+ years of relevant experience for intermediate candidates; 8+ years for senior candidates
- Experience 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; daily communication with English- and French-speaking clients and partners across Canada via email and phone calls
- Undergraduate or master’s degree in a relevant field preferred, not required

Core Competencies

Demonstrates expertise in developing and maintaining analytical data products, building scalable ETL/ELT pipelines, and ensuring data quality and governance. Proficient in translating financial requirements into data solutions while collaborating with investment teams and stakeholders.

Highest-signal resume keywords

- Python Development
- ETL/ELT Pipeline Development
- Data Engineering
- Power BI Solutions
- Google Cloud Platform

ATS Optimization Keywords

Hard Skills

- Data Modeling
- Analytical Data Products
- Transformation Logic
- Data Quality Controls
- Investment Analytics
- Dbt Framework
- BigQuery
- Prefect
- Dagster
- Airflow

Soft Skills

- Collaboration
- Communication
- Problem-Solving

Industry Keywords

- Investment Workflows
- Financial Datasets
- Analytics Engineering
- Data Governance
- Operational Runbooks

Tools & Technologies

- Power BI
- Streamlit
- GitHub
- Docker
- CI/CD

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📌 Senior Analyst, Quantitative Data Science (Quebec City)
🏢 Jobtailor
📍 Quebec City