Senior Full-Stack Data & AI Engineer

Company Overview

Bridgenext is a digital consulting services leader that helps clients innovate with intention and realize their digital aspirations by creating digital products, experiences, and solutions around what real people need. Our global consulting and delivery teams facilitate highly strategic digital initiatives through digital product engineering, automation, data engineering, and infrastructure modernization services, while elevating brands through digital experience, creative content, and customer data analytics services.

Don't just work, thrive. At Bridgenext, you have an opportunity to make a real difference - driving tangible business value for clients, while simultaneously propelling your own career growth. Our flexible and inclusive work culture provides you with the autonomy, resources, and opportunities to succeed.

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.

Responsibilities include but are not limited to:

• 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

Workplace: Hybrid in the Greater Toronto Area

Must Have

Skills:

• 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 No
  
  SQL 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…