Full-Stack Software Engineer - Quantamental & Derivatives Team

Requisition ID: 233208

Join a purpose driven winning team committed to results in an inclusive and high-performing culture.

1832 Asset Management L.P. is one of Canadas largest assets managers and offers a comprehensive range of solutions through Dynamic Funds and ScotiaFunds spanning every major sector geographic region and investment discipline. The firm and its predecessor companies have been providing asset management services in Canada since 1877 as a manager of mutual funds and providing investment solutions for private clients institutions foundations endowments and managed asset programs.

Today the firm has over $100 billion in assets under management and its experienced investment management team is active across capital markets and asset classes deploying traditional and innovative strategies driven to deliver investment management excellence for our clients across our domestic and international businesses.

1832 Asset Management L.P. is a limited partnership the general partner of which is wholly owned by Scotiabank.

As a Quantitative Developer Analyst and core member of our Quantamental & Derivatives (Q&D) group you will collaborate with senior engineers and quants to design build and evolve the technology stack that transforms data and code into investable insights. This role is ideal for an engineer who enjoys hands-on coding learning from experts and contributing to mission-critical systems in a highly collaborative environment. You will work closely with quants data scientists and portfolio managers to deliver robust scalable systems that power systematic investing across global markets.

Is this role right for you In this role you will:

Maintain and enhance the teams current investment infrastructure:
Build Full-Stack Solutions: Develop and maintain web applications services and APIs using Python (Django/FastAPI) and modern JavaScript/TypeScript frameworks (React ) in partnership with senior team members.
Assist with Cloud Migration: Support the migration of on-prem workloads to Azure and other public clouds. Containerize services with Docker define infrastructure as code (IaC) using Terraform and configure Bitbucket Pipelines / Azure DevOps for automated deployments.
DevOps & CI/CD Contribute to automated testing continuous integration monitoring and security hardening to uphold reliability and compliance.
Data Engineering & Database Migration: Help design data models and migrate datasets across PostgreSQL SQL Server BigQuery and time-series stores; ensure data quality and lineage.
Quantitative Workflow Support Productionize research prototypes maintain back-testing pipelines and troubleshoot live execution issues.
Continuous Improvement Follow coding standards participate in peer reviews and surface new tools that can enhance our research and trading platform.

Do you have the skills that will enable you to succeed in this role Wed love to work with you if you have:

5 years of professional software engineering experience delivering production Python/Django applications.
Hands-on experience deploying and operating cloud-native workloads on Azure (bonus: AWS or GCP); strong familiarity with Kubernetes containerization and IaC tooling such as Terraform.
Practical knowledge of Bitbucket (or GitHub/GitLab) and building CI/CD automations with Bitbucket Pipelines and/or Azure DevOps.
Demonstrated success executing database migrations and schema evolution across heterogeneous architectures (e.g. SQL Server PostgreSQL on-prem cloud).
Advanced SQL skills plus experience with NoSQL/time-series data stores (Redis Influx TimescaleDB).
Solid understanding of distributed systems concurrency secure coding and performance optimization.
Exposure to data pipelines (ETL/ELT) event-driven architectures (Pub/Sub Kafka) and financial or time-series data sets.
Bachelors degree in Computer Science Engineering or a related field
Clear concise communication skills and a collaborative mindset.

Wed also love to work with you if you have these preferred (but not required) skills & experience:

Large Language Model (LLM) Experience: Fine-tuning prompt engineering or production deployment of GPT-4o/GPT-4o-mini Gemini 1.5 Claude 3 Llama-3 or similar models.
Retrieval-Augmented Generation (RAG): Design and implementation of RAG pipelines including graphRAG architectures embeddings chunking strategies vector stores (Pinecone pgvector Weaviate Chroma etc.) evaluation metrics and guard-rail tooling.
Model Context Protocol (MCP): Familiarity with orchestrating multi-step or multi-model LLM workflows using MCP standards.
MLOps & LLM-Ops Tooling: Hands-on experience with LangChain LlamaIndex Ray Serve BentoML Feast or comparable frameworks for scalable model serving and monitoring.
Cloud & Data Engineering Expertise: Building and optimizing workloads on GCP (Vertex AI Cloud Run GKE) or equivalent with emphasis on memory optimization and real-time/streaming data processing

Location(s): Canada : Ontario : Toronto

Scotiabank is a leading bank in the Americas. Guided by our purpose: for every future we help our customers their families and their communities achieve success through a broad range of advice products and services including personal and commercial banking wealth management and private banking corporate and investment banking and capital markets.

At Scotiabank we value the unique skills and experiences each individual brings to the Bank and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including but not limited to an accessible interview site alternate format documents ASL Interpreter or Assistive Technology) during the recruitment and selection process please let our Recruitment team know. If you require technical assistance please click here. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however only those candidates who are selected for an interview will be contacted.