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 up-to-date 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

📌 Senior Analyst, Quant System (Winnipeg)
🏢 Jobtailor
📍 Winnipeg