Partner with Front Office teams including Quantitative Equity, Trading, Risk, and Asset Allocation to co-design and co-develop quantitative solutions
Translate research, investment, and analytical workflows into production-ready implementations
Act as a technical counterpart bridging quantitative intent and platform constraints
Support the full lifecycle of quantitative solutions from design and deployment through ongoing evolution
Standardize and operationalize analytical workflows
Collaborate with data engineering partners on reliable, validated, and orchestrated data pipelines
Identify opportunities for AI-assisted capabilities, automation, and intelligent workflow support
Define, implement, and maintain reusable engineering capabilities, solution patterns, and development standards
Contribute Python packages, libraries, frameworks, documentation, standards, and best practices
Improve analytics and quantitative computing environments across production and non-production environments
Contribute to migrations, upgrades, and platform standardization initiatives
Build tooling, automations, and platform capabilities supporting quantitative workflows
Participate in incident triage, root-cause analysis, deployment, monitoring, troubleshooting, and operational support
Design, build, and support data pipelines connecting source data, transformations, model logic, and downstream reporting or application layers
Contribute to reusable packages, solution templates, orchestration patterns, deployment and monitoring tools, shared components, migration patterns, AI-enhanced workflows, and reusable data pipeline templates
Requirements Robust Python software engineering skills and production-grade development practices
Experience designing maintainable, modular, and reusable software solutions




Experience in data-intensive, analytics-heavy, or quantitative environments
Familiarity with cloud-native development environments and shared tooling ecosystems
Working knowledge of Git-based workflows, code reviews, and CI/CD concepts
Experience building internal tools, libraries, automation capabilities, or shared engineering components
Strong debugging and problem-solving across research and production contexts
Ability to diagnose application logic, orchestration, data dependency, and runtime environment issues
Familiarity with modern AI capabilities, AI-assisted development, or applied AI/ML solutions
Experience with ETL/ELT, orchestration, data validation, and data quality patterns
Understanding of time-series analysis, financial instruments, risk concepts, modeling, backtesting, and performance evaluation
Ability to read quantitative logic and translate quantitative requirements into scalable technical solutions
Strong collaboration, communication, ownership, autonomy, and ambiguity-management skills
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 at the intersection of software engineering, analytics, quantitative research, or investment workflows




Demonstrated ability to build and support production-grade technical solutions
Experience in quantitative finance, investment technology, trading, risk, or portfolio management is an asset
Intermediate proficiency in French, required for daily communication with French-speaking clients and partners across Canada via email and phone
Familiarity with orchestration, containerization, automation frameworks, cloud-native architectures, scalable application development, platform engineering, developer enablement, internal tooling, data pipeline orchestration, data quality automation, analytical data products, and production AI integration
Core Competencies Demonstrates strong Python software engineering skills and experience in building production-grade solutions within data-intensive and quantitative environments. Proficient in developing maintainable software, collaborating with cross-functional teams, and implementing AI-assisted capabilities and data pipelines.
Highest-signal resume keywords Python Software Engineering
Data Pipeline Orchestration
AI-Assisted Development
ETL/ELT Processes
Quantitative Finance Expertise
ATS Optimization Keywords Hard Skills Python
Data Validation
Time-Series Analysis
Quantitative Modeling
Debugging
CI/CD Concepts
Orchestration
Containerization
Automation Frameworks
Production-Grade Development
Soft Skills Collaboration
Communication
Ownership
Problem-Solving
Ambiguity Management
Industry Keywords Quantitative Research
Investment Technology
Risk Management
Portfolio Management
Financial Instruments
Tools & Technologies Git
Cloud-Native Environments
Analytical Data Products
Internal Tooling
Shared Engineering Components

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📌 Senior Analyst, Quant System (Quebec City)
🏢 Jobtailor
📍 Quebec City