Senior Software Developer (Canada, EST, Remote)

Role Overview

The Senior Software Developer is a hands-on senior individual contributor who leads complex technical work in their product area in MindBridge. You design and deliver capabilities for secure, scalable financial intelligence spanning data ingestion and analytics, enterprise integrations, business insights, AI-assisted workflows, and the infrastructure patterns that support reliable customer outcomes. This role emphasizes execution and subsystem-level ownership within established architectural direction not platform-wide architecture or roadmap definition, which sit with our Platform Architect and engineering leadership. 

In this role, you will: 

• Own technical delivery and subsystem-level design for initiatives spanning services, data pipelines, APIs, analytical workloads, AI/agent services, and cloud infrastructure
• Help shape standards and technical decisions that affect multiple teams, working within broader architectural strategy
• Provide hands-on technical solutions on ambiguous, high-stakes engineering problems—decomposing requirements, driving design and implementation, mentoring engineers, and improving how we run systems in production
• Partner with Product, Data Science, UX/Design, Security, Infrastructure, Implementation/Professional Services, and other customer-facing teams to ship capabilities that matter to finance, audit, and enterprise customers  

What You Will Do  

• Help lead the technical design and delivery of large, cross-functional initiatives spanning backend services, data systems, APIs, analytics and workflows, AI/agent services, and platform infrastructure
• Design and evolve scalable, secure, maintainable systems for high-volume financial data ingestion and analysis, workflow orchestration, AI/agent capabilities, and enterprise integrations
• Build and extend platform capabilities that support AI/agent systems configuration, insight generation, explanation agents, and orchestration patterns
• Identify technical risks, bottlenecks, reliability gaps, and architectural opportunities; recommend solutions that improve scalability, maintainability, security, and delivery speed.
• Help lead implementation of complex initiatives while remaining hands-on in design, code review, critical-path development, debugging, and production readiness.
• Raise the bar on engineering excellence through design reviews, coding standards, documentation, testing, observability, incident response, and operational discipline
• Work through production incidents, root cause analysis, remediation, and longer-term reliability improvements
• Evaluate technologies and practices that improve product quality, analytical performance, developer productivity, and operational resilience.
• Contribute to platform standards for API design, data modeling, asynchronous processing, CI/CD, security, and observability 

What You Bring to the Role  

• 5+ years of professional software engineering experience, including significant experience building and operating enterprise SaaS, data-intensive, or large-scale distributed systems
• Demonstrated Senior-level technical solutions: implementing ambiguous  team initiatives, influencing without formal authority, and owning outcomes across multiple subsystems or architectural layers
• Strong experience designing secure, reliable, multi-tenant SaaS systems with attention to data isolation, access control, auditability, scalability, and operational supportability
• Hands-on experience with data-intensive systems, such as ingestion pipelines, ETL/ELT, batch or streaming processing, analytical workflows, data validation, and warehouse/lakehouse integrations
• Strong fundamentals in API design, service integration, data modeling, performance optimization, asynchronous processing, and distributed systems trade-offs
• Working knowledge of production AI/ML systems, prompt orchestration, RAG, or agent workflow patterns, and the ability to reason about latency, cost, quality, and governance trade-offs
• Experience with relational and/or analytical database technologies and the ability to reason about schema design, query performance, storage patterns, and data lifecycle concerns
• Working knowledge of cloud-native engineering practices, including cloud platforms, containerized workloads, CI/CD, infrastructure automation, observability, and production operations
• Ability to collaborate effectively with Product, Data Science, UX/Design, Security, Infrastructure, Customer Success, and other stakeholders.
• Strong written and verbal communication skills, including the ability to explain architectural trade-offs to both technical and non-technical audiences
• Experience working in Agile, cross-functional engineering environments 

Preferred Qualifications 

• Experience in fintech, audit, accounting, risk, compliance, or other regulated/high-trust enterprise environments
• Proficiency with Java/Spring Boot, Python, or comparable backend/data engineering frameworks
• Experience with Azure or Microsoft data-platform ecosystems, including Azure Data Factory, Microsoft Fabric, or Power BI integrations
• Experience with Databricks, Snowflake, lakehouse architectures, Apache Iceberg, Delta Lake, or similar analytical storage and processing patterns
• Experience building systems that support AI/ML workloads, LLM-assisted workflows, RAG systems, or agent applications where explainability, auditability, and governance matter
• Experience with Infrastructure as Code, Kubernetes, observability platforms, SRE practices, and incident management
• Familiarity with frontend architecture concerns such as React, design systems, and shared product capabilities
• Experience designing customer-facing APIs, SDKs, developer tooling, or enterprise integration platforms 

Core Competencies 

• Technical scope: Operates across multiple subsystems or architectural layers. Defines patterns and standards that span products.
• Technical skill: Applies deep expertise across core technologies and design paradigms. Evaluates trade-offs for performance, scalability, security, reliability, explainability, AI output quality, agent behavior, maintainability.
• Responsibility: Owns outcomes for complex initiatives. Ensures the long-term health of systems in scope. Advises engineering leadership on technical options, risks, sequencing, and sustainability.
• Autonomy and collaboration: Communicates clearly with fellow engineers, product managers, data scientists, security partners, customer-facing teams, and senior stakeholders.
• Impact: Improves product quality, customer outcomes, engineering velocity, platform reliability, and the safe adoption of AI and agent capabilities through sustained technical solutions.