Job Description

Job Title: Azure Data Engineer

Location: Toronto, ON (Hybrid)

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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