Senior Data & Analytics Architect

VDart Inc; Toronto; On-site; CAD 120,000 - 150,000; Full time

Job summary

A leading data solutions provider in Toronto is seeking a Senior Data & Analytics Architect to drive the development of cloud-native data and AI platforms. This role requires extensive experience in data architecture, specifically with Databricks and Azure. The ideal candidate will lead innovative projects that shape scalable Lakehouse architectures and enable enterprise-wide analytics capabilities.

Qualifications

• 8 years of experience in data architecture, engineering or analytics.
• Deep expertise in Databricks Unity Catalog, Azure and/or AWS data and ML services.
• Strong grasp of Lakehouse architecture, data governance and MLOps.

Responsibilities

• Lead the development of cloud-native data and AI platforms.
• Shape scalable Lakehouse architectures.
• Design solutions for LLM fine-tuning and deployment.

Skills

Fund Management

Drafting

End User Support

Infrastructure

Airlines

Catia

Education

Bachelors or Masters in Computer Science, Data Engineering or related field

Tools

Databricks

Azure Data Factory

Power BI

MLflow

Apache Spark

Job description

Position Overview

Role: Senior Data & Analytics Architect

Location: Toronto ON

Contract

Role

Our delivery is looking for a Senior Data & Analytics Architect to lead the development of next-generation cloud-native data and AI platforms. This role is pivotal in shaping scalable Lakehouse architectures and enabling enterprise-wide analytics, machine learning and generative AI capabilities. Technologies like Azure, AWS, Databricks and Unity Catalog to drive innovation and deliver measurable business impact.

Cloud & Lakehouse Architecture

• Architect and deploy Lakehouse platforms using Databricks on Azure, Delta Lake, Apache Spark and ADLS Gen2.
• Build and optimize data pipelines with Databricks Workflows and Azure Data Factory for structured and unstructured data.
• Implement secure, compliant data governance and lineage using Unity Catalog.
• Enable real-time and batch analytics with Databricks Structured Streaming, Azure Event Hubs and Amazon Kinesis.
• Design, build and maintain scalable data pipelines and workflows using Azure Data Factory and Databricks leveraging Spark / PySpark frameworks.

Machine Learning & Generative AI

• Collaborate with data science teams to deploy ML and GenAI models using Databricks ML.
• Design solutions for LLM fine-tuning and deployment via Databricks Model Serving, Azure OpenAI and Amazon Bedrock.
• Manage feature stores, model registries and experiment tracking with MLflow and cloud-native MLOps tools.
• Ensure production-grade scalability, monitoring and governance of models.

MLOps & Platform Engineering

• Develop CI / CD pipelines for ML and data workflows using Databricks Repos, Azure DevOps and GitHub Actions.
• Automate workflows with Databricks Jobs and Azure ML Pipelines.
• Optimize compute infrastructure (Databricks clusters, AKS, EKS) for training and inference.
• Implement observability and model drift detection with Databricks monitoring and Azure Monitor.

Strategic Leadership

• Translate business goals into scalable data and AI solutions in partnership with both business and technical stakeholders.
• Mentor data engineers, ML engineers and analytics professionals.
• Evaluate and recommend emerging GenAI frameworks and cloud-native tools.
• Champion data democratization and self-service analytics with Power BI and Databricks SQL.

Resource Profile

• Bachelors or Masters in Computer Science, Data Engineering or related field.
• 8 years of experience in data architecture, engineering or analytics.
• Preferred certifications: Databricks Certified Data Engineer or Architect; Azure Solutions Architect Expert; AWS Certified Solutions Architect; Azure or AWS Machine Learning Specialty.
• Deep expertise in Databricks Unity Catalog, Azure and/or AWS data and ML services.
• Strong grasp of Lakehouse architecture, data governance and MLOps.
• Hands-on experience with Databricks Unity Catalog, MLflow, LLMs and GenAI frameworks.
• Proven proficiency in Databricks including building pipelines and workflows and a solid understanding of Spark / PySpark.
• Expertise with Azure Data Factory, Databricks SQL data pipeline development and Power BI reporting.
• Excellent communication and stakeholder engagement skills.

Key Skills

• Fund Management
• Drafting
• End User Support
• Infrastructure
• Airlines
• Catia

Employment Type

Full-time

Experience

years

Vacancy

1