Full-Time

Technical Architect

ShyftLabs

ShyftLabs

11-50 employees

Data-driven decision-making platform for organizations

Compensation Overview

CA$125k - CA$175k/yr

Toronto, ON, Canada

Hybrid

Three days per week on-site in Toronto.

Category
Software Engineering (1)
Required Skills
Agile
Python
JavaScript
React.js
Git
Data Structures & Algorithms
Graph Databases
SQL
Machine Learning
Postgres
ETL
Docker
AWS
REST APIs
Data Analysis
Requirements
  • 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.
Responsibilities
  • 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.

ShyftLabs helps organizations adopt a data-first approach to decision making by designing and implementing processes that turn data into actionable insights. Its solution builds structured analytics workflows and governance, so teams access trustworthy data, follow defined steps, and act on results with clarity. Unlike tools that only show dashboards, ShyftLabs focuses on repeatable data practices and governance that speed up decisions and reduce ad hoc analysis. The goal is to help organizations stay ahead of the competition by enabling faster, more informed decisions across the business.

Company Size

11-50

Company Stage

N/A

Total Funding

N/A

Headquarters

Canada

Founded

2018

Simplify Jobs

Simplify's Take

What believers are saying

  • ShyftLabs hires Apache Druid Engineers and Data Architects in Gurugram and Toronto.
  • ShyftLabs powers 200+ experts streamlining operations for major public agencies.
  • ShyftLabs modernizes legacy systems with cloud migration for scalable citizen services.

What critics are saying

  • Databricks Lakehouse erodes consulting margins as clients build in-house pipelines.
  • Snowflake Cortex AI bypasses ShyftLabs BI with native serverless ML functions.
  • Talent exodus to AWS drains ShyftLabs engineers amid 30% higher salaries.

What makes ShyftLabs unique

  • ShyftLabs delivers privacy-first Carter platform for secure public sector AI.
  • ShyftLabs unlocked $500 million value via data and AI for retailers and health.
  • ShyftLabs builds custom low-latency pipelines embedding intelligence for real-time decisions.

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Benefits

Health Insurance

Hybrid Work Options

Professional Development Budget