Data Engineer

Job summary

Build, maintain, and optimize scalable data pipelines using Databricks, Spark/PySpark, and SQL, integrating data from Azure storage and other systems for analytics and BI. Improve workload performance and reliability, implement governance and security controls with Unity Catalog, and deploy Databricks artifacts across environments using Databricks Asset Bundles.

Job details

Role : Azure Data Engineer with Databricks Expertise Location : Ottawa/Toronto (Hybrid) Job Summary: We are seeking highly skilled Azure Data Engineer with strong expertise in SQL, Python, Datawarehouse, Cloud ETL tools to join our data team. The ideal candidate will design, implement and optimize large-scale data pipeline, ensuring scalability, reliability and performance. This role involves working closely with multiple teams and business stakeholders to deliver cutting-edge data solutions. Key Responsibilities: Data Pipeline Development: Build and maintain scalable ETL/ELT pipelines using Databricks. Leverage PySpark/Spark and SQL to transform and process large datasets. Integrate data from multiple sources including Azure Blob Storage, ADLS and other relational/non-relational systems. Collaboration & Analysis: Work Closely with multiple teams to prepare data for dashboard and BI Tools. Collaborate with cross-functional teams to understand business requirements and deliver tailored data solutions. Performance & Optimization: Optimize Databricks workloads for cost efficiency and performance. Monitor and troubleshoot data pipelines to ensure reliability and accuracy. Governance & Security: Implement and manage data security, access controls and governance standards using Unity Catalog. Ensure compliance with organizational and regulatory data policies. Deployment: Leverage Databricks Asset Bundles for seamless deployment of Databricks jobs, notebooks and configurations across environments. Manage version control for Databricks artifacts and collaborate with team to maintain development best practices. Technical Skills: Strong expertise in Databricks (Delta Lake, Unity Catalog, Lakehouse Architecture, Table Triggers, Delta Live Pipelines, Databricks Runtime etc.) Proficiency in Azure Cloud Services. Solid Understanding of Spark and PySpark for big data processing. Experience in relational databases. Knowledge on Databricks Asset Bundles and GitLab. Preferred Experience: Familiarity with Databricks Runtimes and advanced configurations. Knowledge of streaming frameworks like Spark Streaming. Experience in developing real-time data solutions. Certifications: Azure Data Engineer Associate or Databricks certified Data Engineer Associate certification. (Optional)

What you’ll do

Build, maintain, and optimize scalable data pipelines using Databricks, Spark/PySpark, and SQL, integrating data from Azure storage and other systems for analytics and BI. Improve workload performance and reliability, implement governance and security controls with Unity Catalog, and deploy Databricks artifacts across environments using Databricks Asset Bundles.

Requirements

The role requires strong Databricks expertise, proficiency with Azure cloud services, solid Spark and PySpark skills, and experience with relational databases and large-scale data processing. Knowledge of Databricks Asset Bundles and GitLab is expected; streaming and real-time data experience and Azure or Databricks data engineering certifications are preferred or optional.

Listed skills

• SQL · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Databricks
• Azure Cloud Services
• SQL
• Python
• PySpark
• Apache Spark
• ETL/ELT Pipelines
• Data Warehousing
• Delta Lake
• Unity Catalog
• Lakehouse Architecture
• Databricks Asset Bundles
• GitLab
• Data Governance
• Spark Streaming
• Relational Databases

Job areas

• Data & Analytics
• Technology
• Software

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