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

Strong 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 contemporary 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 (Winnipeg)
🏢 Jobtailor
📍 Winnipeg