Senior Analyst, Quantitative Data Science

Jobtailor

Quebec

On-site

CAD 90,000 - 140,000

Full time

2 days ago

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Job summary

Jobtailor seeks a seasoned data/analytics engineer to build scalable investment data platforms. You will partner with Portfolio Management and analytics teams to translate financial requirements into robust data solutions and governance.

Strong Python, dbt, GCP, and Power BI skills are essential, with advanced French for client communication across Canada. Role emphasizes end-to-end data pipelines, semantic modeling, and production-grade analytics.

Qualifications

Strong Python development skills.
Experience with 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.
Advanced proficiency in French; daily communication with English- and French-speaking clients and partners across Canada via email and phone calls.

Responsibilities

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.
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.

Skills

Python

Data Engineering

Analytics Engineering

Data Modeling

Power BI Solutions

Google Cloud Platform

dbt

Prefect

Dagster

Airflow

GitHub

Docker

CI/CD

Data Quality

Investment Analytics

French (Advanced)

Education

Undergraduate or Master’s degree in a relevant field

Tools

Power BI

Streamlit

GitHub

Docker

CI/CD

BigQuery

dbt Framework

Prefect

Dagster

Airflow

Job description

• 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
Strong 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.

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