Senior Software Engineer, Data Product in Canada Creek, Nova Scotia at Jobgether
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Job Description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Software Engineer, Data Product based in Canada.
This is a high-impact engineering role focused on building and scaling the data and intelligence layer that powers modern e-commerce logistics decisions. You will design and operate backend systems that transform complex shipping and operational data into real-time predictions, recommendations, and configurable decision engines for customers. The role sits at the intersection of backend engineering, machine learning systems, and platform architecture, requiring both strong software craftsmanship and production ML experience. You will own services that serve model outputs at scale, ensuring reliability, low latency, and observability across critical customer-facing workflows. Working closely with data science and platform teams, you will help bridge experimentation and production, shaping how ML-powered features are delivered end-to-end. This is a senior technical position with significant ownership, influence over architecture, and responsibility for production-grade ML systems. You will also help define engineering standards and elevate how data products are built across the organization.
In this role, you will be responsible for designing, building, and operating backend and ML-powered systems that support data-driven product experiences at scale. You will ensure that predictive systems are reliable, observable, and optimized for real-world production constraints.
- Build and maintain backend services that deliver ML-based predictions and data-driven features through high-performance APIs
- Design scalable Python-based services supporting low-latency and high-throughput workloads
- Own the end-to-end lifecycle of ML-powered services, including deployment, monitoring, incident response, and continuous improvement
- Develop and maintain feature pipelines bridging offline model training with online inference systems
- Lead API design, system decomposition, and technical architecture reviews across data product surfaces
- Implement and improve MLOps practices including model versioning, rollout strategies, A/B testing, and rollback mechanisms
- Instrument systems for observability including latency, throughput, drift detection, and prediction quality monitoring
- Partner with Data Science to operationalize models and improve production performance
- Drive engineering best practices through code reviews, design leadership, and mentorship
- Evaluate and introduce tools and frameworks that improve ML serving, reliability, and developer velocity
You bring deep backend engineering expertise combined with hands-on experience delivering machine learning systems in production environments. You are comfortable owning complex distributed systems and bridging the gap between data science and engineering.
- 8+ years of backend software engineering experience, with significant exposure to ML-powered systems in production
- Strong expertise in Python backend development, ideally with async frameworks (e.g., FastAPI)
- Solid understanding of PostgreSQL, distributed systems, and event-driven architectures (e.g., Kafka)
- Proven experience deploying and maintaining ML models as production APIs, not just training models
- Hands-on experience with ML lifecycle tooling (e.g., MLflow or equivalent) and production model management practices
- Strong understanding of MLOps concepts such as model versioning, canary releases, shadow deployments, and A/B testing
- Ability to design observability frameworks for ML systems, including monitoring drift and prediction quality
- Experience leading technical design discussions and influencing architecture across teams
- Strong communication skills with the ability to explain technical and ML concepts to diverse audiences
- Collaborative mindset with strong partnership skills across Data Science and Engineering teams
- Competitive compensation package aligned with senior-level responsibilities
- Remote-first flexibility across Canada
- Opportunity to work on globally impactful logistics and data infrastructure products
- Strong focus on engineering autonomy, ownership, and technical leadership
- Collaborative, distributed team culture with high trust and low bureaucracy
- Access to modern ML and data platforms, including production-grade MLOps tooling
- Opportunities for professional growth, mentorship, and technical leadership development
- Inclusive work environment supporting diverse backgrounds and perspectives.