Job Title: Azure Data Engineer Primary Skills: Azure Databricks, PySpark, SQL & ETL/Data Engineering Location: Toronto, ON Work Model: Hybrid Experience: 5+ Years Cloud Platform: Microsoft Azure Job Summary We are seeking an experienced Azure Data Engineer to design, develop, and support scalable data ingestion and processing solutions on the Microsoft Azure platform.

The ideal candidate will have strong hands-on experience with Azure Databricks, PySpark, SQL, and Azure Data Factory.

The role involves building reusable data engineering frameworks, developing end-to-end data pipelines, supporting batch and streaming workloads, and ensuring data quality, reliability, and consistency across the data platform.

The candidate must also possess strong troubleshooting skills and experience providing production support across L1, L2, and L3 support levels.

Key Responsibilities Design, develop, and implement data ingestion pipelines from multiple structured and unstructured data sources using Azure Databricks.

Build scalable, configurable, and reusable data ingestion frameworks to support multiple datasets and source systems.

Develop and maintain end-to-end data pipelines, moving data from source systems through transformation layers into target data repositories.

Implement data transformation and processing solutions using PySpark, Spark SQL, and SQL.

Develop data validation, reconciliation, error handling, audit logging, and data quality mechanisms.

Design and develop both batch and real-time streaming data pipelines.

Work with event-driven and streaming technologies such as Apache Kafka, Azure Event Hubs, and Spark Structured Streaming.





Develop and orchestrate data pipelines using Azure Data Factory (ADF) and integrate them with Azure Databricks.

Work with Azure data services, including Azure Data Lake Storage Gen2 (ADLS Gen2) and other target repositories.

Optimize Databricks and Spark workloads for performance, scalability, and reliability.

Collaborate with cross-functional teams responsible for APIs, search capabilities, source applications, and downstream systems.

Evaluate data engineering tools and technologies based on functional, technical, performance, and business requirements.

Troubleshoot data pipeline failures, perform root-cause analysis, and implement permanent fixes.

Provide L1, L2, and L3 production support, including operational monitoring, incident resolution, defect fixing, and problem management.

Maintain technical documentation, operational procedures, and data pipeline specifications.

Communicate effectively with technical teams, business stakeholders, and customer teams.

Mandatory Technical Skills Strong hands-on experience with Azure Databricks.

Excellent knowledge of Apache Spark and PySpark.

Advanced SQL skills.

Experience with Azure Data Factory (ADF).

Solid experience building ETL/ELT and data ingestion pipelines.





Experience with Azure Data Lake Storage Gen2 (ADLS Gen2) and Azure Storage.

Strong understanding of Delta Lake and Delta Tables.

Experience developing reusable and configuration-driven data engineering frameworks.

Knowledge of batch and real-time/streaming data processing.

Experience with Kafka, Azure Event Hubs, or Spark Structured Streaming.

Understanding of data warehousing and data lake concepts.

Experience implementing data quality, reconciliation, logging, auditing, and exception-handling mechanisms.

Strong production troubleshooting and L1/L2/L3 application or data platform support experience.

Good to Have Skills Databricks Workflows and Jobs.

Delta Live Tables / Lakeflow Declarative Pipelines.

Databricks Unity Catalog. CI/CD experience using Azure Dev

Ops or Git.

Strong Python programming skills.

Azure Key Vault and secrets management. REST API integration.

Experience working with JSON, CSV, Parquet, and other data formats.

Data migration and modernization experience.

Understanding of Medallion Architecture (Bronze, Silver, and Gold layers).

Monitoring, alerting, and operational support experience.

Soft Skills Strong verbal and written communication skills.

Excellent analytical and problem-solving abilities.

Ability to independently troubleshoot complex data engineering issues.

Comfortable working with distributed and cross-functional project teams.

Ability to communicate technical issues clearly to technical and non-technical stakeholders.

Strong ownership and accountability for production systems.

📌 Azure Data Engineer (Toronto)
🏢 AceStack
📍 Toronto