Azure Data Engineer
Job Title: Azure Data Engineer
Location: Chicago, IL - must do one day week onsite
Length: 6 months
This will require an onsite interview or require the candidate to pick up their laptop in person
Requirements/Top Skills Needed:
ADF
Databricks
Python
SQL
Retail experience would be helpful, but not required
Job Title: Azure Data Engineer
Job Summary: We are seeking a highly skilled Azure Data Engineer to design, build, and optimize our next-generation cloud data platform. You will build scalable data pipelines, manage enterprise data warehouses, and transform massive datasets using Azure Cloud, ADF, Databricks, Spark, and PySpark. Key Responsibilities
· Pipeline Orchestration: Design and implement scalable ETL/ELT pipelines using Azure Data Factory (ADF).
· Big Data Processing: Build high-performance, distributed data transformation workloads via Azure Databricks and Apache Spark/PySpark.
· Data Warehousing: Architect, model, and maintain cloud-native Data Warehouses and Data Lakes (ADLS Gen2).
· Architecture Implementation: Apply modern architectural patterns like the Medallion Architecture (Bronze, Silver, Gold layers) and Delta Lake formats.
· Performance Tuning: Optimize Spark jobs, cluster configurations, and SQL queries to minimize latency and cloud compute costs.
· Governance & Security: Implement security frameworks, data lineage, and user access policies using Unity Catalog.
· CI/CD Automation: Deploy, monitor, and automate workflows using Azure DevOps and automated alert configurations. Required Technical Skills:
· Cloud Platform: Microsoft Azure Ecosystem (ADLS Gen2, Azure SQL, Synapse Analytics, Key Vault, Storages, Azure Functions, Logic Apps).
· Data Orchestration: Advanced proficiency in Azure Data Factory (ADF).
· Compute & Analytics: Deep hands-on experience with Azure Databricks.
· Big Data Frameworks: Strong mastery of Apache Spark and PySpark (including Spark SQL).
· Languages: Expert level in Python and SQL.
· Data Warehousing: Solid understanding of Data Modeling (Star/Snowflake schema) and warehousing concepts
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