Job Title: Azure Data Engineer
Location: Toronto, ON
This role requires deep technical expertise in Azure data services, modern data architecture, and best practices in data engineering, security, and automation.
Key Responsibilities:
Architecture & Solution Design:
Architect cloud-native data solutions using Azure Data Lake, Synapse, Databricks, and Data Factory.
Define the end-to-end data architecture including ingestion, transformation, modeling, storage, and consumption layers.
Lead the adoption of Delta Lake, lakehouse patterns, streaming architectures, and medallion models.
Evaluate new Azure capabilities and make recommendations to improve data strategy and platform maturity.
Data Pipeline & Platform Development:
Build highly scalable, fault-tolerant ETL/ELT pipelines using ADF, Databricks, Synapse pipelines, and Azure Functions.
Write complex transformations using PySpark, SQL, Python, and Spark best practices.
Optimize pipeline performance, cost efficiency, and reliability through tuning and automation.
Data Governance, Quality & Security:
Establish data quality frameworks, validation rules, observability, and automated testing.
Implement enterprise-grade security including RBAC, Key Vault integration, encryption, and audit controls.
Work with governance teams to enable lineage, metadata management, and cataloging with Azure Purview.
Leadership & Collaboration:
Mentor and guide junior/intermediate data engineers on Azure best practices.
Partner with architects, data scientists, BI developers, and business teams to deliver high-impact data solutions.
Lead technical design reviews, code reviews, and cloud architecture discussions.
DevOps & Automation:
Develop CI/CD pipelines using Azure DevOps or GitHub Actions for automated deployment of data workloads.
Create and maintain Infrastructure as Code (IaC) using ARM/Bicep or Terraform.
Implement monitoring and logging using Azure Monitor, Log Analytics, and Databricks monitoring tools.
Required Qualifications:
7 12+ years of experience in data engineering or software engineering.
Advanced experience with Azure Data Factory, Azure Databricks, Azure Synapse Analytics, ADLS Gen2.
Expert proficiency in SQL, PySpark, and Python.
Strong experience designing large-scale data architectures (lakehouse, data warehouse, streaming).
Solid understanding of advanced ETL/ELT patterns, orchestration, and distributed computing.
Proven experience optimizing Spark clusters, query performance, and cloud spend.
Hands-on experience implementing CI/CD and IaC in Azure environments.
Strong knowledge of Azure security best practices (managed identities, RBAC, networking, encryption).
Preferred Qualifications:
Experience building streaming pipelines using Event Hub / Kafka / Spark Structured Streaming.
Background enabling ML/AI data pipelines or feature stores.
Familiarity with Power BI, semantic models, and analytics ecosystems.
Azure certifications: DP-203, DP-500, AZ-305, or similar.
Experience in regulated industries (finance, insurance, public sector).
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