Technical Architect

Canada·TorontoFull-Timemid

EngineeringOtherSoftware Architect

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Quick Summary

Overview

About ShyftLabs At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact.

Technical Tools

awsdockerjavascriptpostgresqlpythonreactsqlagiledocumentationetlmachine-learningrest-apissql-optimizationsystem-design

About ShyftLabs

At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth with cutting-edge digital solutions that transform industries and create measurable business impact. We’re growing fast and we’re looking for passionate problem-solvers who are ready to turn big ideas into real outcomes.

The Opportunity

We’re looking for a Technical Architect who can help us develop and scale a variety of platforms and systems. This role will work closely with the Product team to plan new features, guide engineers through implementation and testing, and collaborate across multiple internal teams to streamline integrations and cross-system solutions.

Design and influence the development of service-based systems (both front-end and back-end) with an emphasis on scalable, event-driven architectures.

Collaborate with the Product team to understand requirements, provide estimates, assess technical complexity, and identify dependencies, risks, and roadblocks.

Create lightweight proofs of concept, including those involving graph-based data models, vector embeddings, or ML-driven workflows to validate solution approaches.

Develop secure, scalable, and high-quality features that contribute directly to overall product growth.

Lead architectural efforts across data ingestion pipelines, ensuring seamless integration with internal systems, third-party APIs, and large-scale data processing frameworks.

Design and optimize solutions leveraging Graph Databases (e.g., Neo4j, Amazon Neptune) for complex relationship modelling, knowledge graphs, and advanced analytical capabilities.

Partner with Data Engineering and AI teams to architect systems that support machine learning model integration, feature engineering, and real-time or batch inference workloads.

Contribute to the evolution of platform intelligence, including AI-driven automation, semantic search, recommendation engines, and anomaly detection.

Create and review technical documentation including architecture diagrams, data flow diagrams, data lineage, API specifications, schemas, and integration patterns

Advocate for best engineering practices and mentor developers to grow their technical capabilities.

Work cross-functionally to anticipate system challenges, recommend optimal design patterns, and propose alternative solutions when needed.

Lead the design and development efforts for systems integration projects, ensuring architecture consistency and long-term maintainability.

Act as a key technical expert, addressing issues related to system interoperability, scalability, and architectural decision-making.

Generate and maintain non-functional requirements, technical specifications, SLAs, and performance baselines.

8+ years of experience developing software across front-end and back-end systems, with a history of building scalable technology platforms.

3+ years of software architecture and system design experience.

BA/BS in Computer Science or related field (or equivalent practical experience).

Expert-level knowledge with: Python, React, JavaScript, Docker, Git, REST APIs, Postgres, SQL.

Strong background with AWS services such as S3, CloudFront, EC2, RDS, Batch, Lambda, IAM, and event-driven architecture.

Hands-on experience with Graph Databases (e.g., Neo4j, Amazon Neptune) including data modelling, query optimization, and integration into large-scale systems.

Experience designing and maintaining data ingestion pipelines, streaming or batch ETL, and structured/unstructured data processing.

Exposure to AI/ML workflows, including integrating ML models, embedding generation, inference orchestration, or using cloud-based AI/ML services.

Extensive knowledge of common data structures, algorithms, design patterns, and software engineering best practices.

Strong communication skills with the ability to articulate complex technical concepts clearly and concisely.

Adaptability and comfort working in fast-paced, evolving environments.

Familiarity with Kanban and Agile methodologies and the full Software Development Lifecycle.

Positive attitude and proven ability to build strong relationships across teams.

Ability to evaluate systems and code to ensure compliance with security and architectural standards.