Senior Full-Stack Data & AI Engineer

Bridgenext Digital Engineering•Toronto, ON

13d•CA$130,000 - CA$150,000•Hybrid

About The Position

Bridgenext is seeking a Senior Full-Stack Data & AI Engineer to own the complete data lifecycle—from ingestion and engineering through data modeling, analytics, AI enablement, and front-end consumption—within a cloud-native Azure ecosystem. The role requires strong hands-on expertise in Python-based data engineering, analytics, and AI integration using FastAPI, along with the ability to build or support dashboards and data-driven front-end applications that deliver business-ready outputs. The Senior Engineer will be responsible for owning the full data product lifecycle—requirements, build, deploy, run, and optimize—delivering reusable, governed, high-quality data assets and integrating RESTful APIs with enterprise platforms. Solutions are expected to run on Azure Kubernetes Service (AKS) with built-in authentication, authorization, and scalability. This role focuses on delivering robust, production-ready data products with a data product mindset—reusable, governed, and aligned to business outcomes—within an existing Azure-centric framework. It is positioned as a full-stack Data & AI Engineering role, not a traditional full-stack development position.

Requirements

8+ years of professional experience in data engineering, analytics engineering, or full-stack data platform development
Experience building or supporting dashboards and data-driven applications using tools such as Power BI, React, or similar frameworks
Strong experience building RESTful APIs using FastAPI
Expertise in SQL databases (PostgreSQL, MySQL, SQL Server) with strong data modeling skills (conceptual, logical, physical models and semantic layers)
Experience with NoSQL databases such as MongoDB, DynamoDB, or Redis for diverse data storage needs
Hands-on experience deploying containerized data and AI applications on AKS
Experience enabling ML/LLM use cases including data preparation for training/inference, RAG, chat, and summarization
Experience integrating data pipelines with Azure OpenAI and other AI services
Strong proficiency in Python programming with a data product mindset—building reusable, governed, high-quality data assets aligned to business outcomes

Nice To Haves

Good understanding of Agentic AI frameworks such as LangChain or AutoGen
Exposure to Agent-to-Agent (A2A) communication and agent scaling
Azure data platform experience including Data Factory, Synapse, Purview, Entra ID fundamentals, and app registrations
Knowledge of OAuth2, OIDC, SSO, and SAML configurations; familiarity with data governance and cataloging tools

Responsibilities

Design, develop, and own end-to-end data solutions spanning data ingestion, engineering, modeling, analytics, AI, and front-end consumption
Build and maintain RESTful APIs using FastAPI with authentication, rate limiting, pagination, and error handling
Develop scalable data pipelines and backend services using Python for data ingestion, transformation, and orchestration
Build or support dashboards and data-driven applications (e.g., Power BI, React UI) to enable front-end consumption of data products and KPIs
Design and implement conceptual, logical, and physical data models; build and maintain semantic layers to ensure consistent, governed data access
Deploy and operate containerized data and AI applications on Azure Kubernetes Service (AKS)
Enable ML/LLM use cases including chat, summarization, RAG, agents, and evaluators; prepare and manage data for model training and inference
Integrate data pipelines and applications with Azure OpenAI and other AI services to power intelligent, data-driven features
Deliver analysis-ready datasets, KPIs, and business-ready outputs aligned to stakeholder requirements; collaborate with cross-functional teams and participate in code reviews
Own the full lifecycle of data products: requirements gathering, build, deployment, operational monitoring, and continuous optimization