About the role
Role: AZURE DATA ENGINEER + POWER BI Location-: Toronto, ON Hybrid Hire Type-: Full Time
Data Engineer, BI and delivering on business programs/intakes All our data in on cloud so working experience with MS Azure, SQL, Python and Power BI
Data Modelling & Architecture-:
Strong understanding of semantic models used in enterprise reporting and analytics. Expertise in dimensional modelling, including star and snowflake schema design. Practical experience implementing Slowly Changing Dimensions (SCD) Type 1 and Type 2. Ability to translate business requirements into scalable data models. Advanced Spark SQL Proficiency in Spark SQL, including complex joins, aggregations, and transformations. Strong command over window functions, ranking functions, and analytical queries. Ability to troubleshoot performance issues and optimize Spark SQL queries. PySpark Development Working knowledge of PySpark for data ingestion, transformation, and workflow orchestration. Ability to write and understand basic pyspark functions Basic understanding of Spark architecture, including executors, partitions, and caching.
Data Engineering Skills-:
Experience building ETL/ELT pipelines using Azure Databricks and load to Synapse. Familiarity with Delta Lake, data versioning, and ACID transactions. Independence & Collaboration Ability to work independently with minimal oversight. Strong communication skills to collaborate with cross-functional teams and business stakeholders. Proactive in identifying issues and proposing data-driven solutions.
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About the role
Role: AZURE DATA ENGINEER + POWER BI Location-: Toronto, ON Hybrid Hire Type-: Full Time
Data Engineer, BI and delivering on business programs/intakes All our data in on cloud so working experience with MS Azure, SQL, Python and Power BI
Data Modelling & Architecture-:
Strong understanding of semantic models used in enterprise reporting and analytics. Expertise in dimensional modelling, including star and snowflake schema design. Practical experience implementing Slowly Changing Dimensions (SCD) Type 1 and Type 2. Ability to translate business requirements into scalable data models. Advanced Spark SQL Proficiency in Spark SQL, including complex joins, aggregations, and transformations. Strong command over window functions, ranking functions, and analytical queries. Ability to troubleshoot performance issues and optimize Spark SQL queries. PySpark Development Working knowledge of PySpark for data ingestion, transformation, and workflow orchestration. Ability to write and understand basic pyspark functions Basic understanding of Spark architecture, including executors, partitions, and caching.
Data Engineering Skills-:
Experience building ETL/ELT pipelines using Azure Databricks and load to Synapse. Familiarity with Delta Lake, data versioning, and ACID transactions. Independence & Collaboration Ability to work independently with minimal oversight. Strong communication skills to collaborate with cross-functional teams and business stakeholders. Proactive in identifying issues and proposing data-driven solutions.