Associate Data Engineer ( Snowflake on Microsoft Azure)

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Title: Associate Data Engineer ( Snowflake on Microsoft Azure)

Location: Sandy Springs, GA (suburb of Atlanta) 4 days onsite a week, Local Only

Duration: 6 Months Contract

and GC

Video Interview

Job Description:

The Data Engineer will design, build, and optimize cloud data solutions using Snowflake on Microsoft Azure. The role focuses on scalable data pipelines, robust data models, and secure, performant analytics capabilities for business stakeholders.

Key Responsibilities

• Design, develop, and maintain ELT/ETL data pipelines using Azure Data Factory (ADF)and related Azure services.
• Build and manage Snowflake databases, schemas, tables, views, streams, tasks, and stored procedures.
• Integrate data from diverse sources into Azure Data Lake Storage and Snowflake, handling both structured and semi structured data.
• Implement and monitor data quality checks, error handling, and observability for pipelines across Azure and Snowflake.
• Optimize Snowflake warehouses and Azure resources for performance and cost (query tuning, warehouse sizing, partitioning/clustering, scheduling).
• Apply Azure security and governance best practices, including RBAC, key vault, and network/security configuration, alongside Snowflake roles and access controls.
• Collaborate with data analysts, data scientists, and business teams to translate requirements into end to end solutions on Azure Snowflake.
• Use CI/CD (e.g., Azure DevOps/GitHub Actions) for version control, automated testing, and deployment of data engineering assets.
  
  

Required Skills & Experience

• Strong hands on experience with Snowflake (advanced SQL, performance tuning, Snowpipe/streams/tasks, stored procedures).
• Practical experience building production data pipelines on Azure (ADF; plus nice if Synapse, Databricks, or Functions).
• Proficiency in SQL and at least one scripting/programming language (e.g., Python or PowerShell).
• Solid understanding of data warehousing concepts, dimensional modeling, and ETL/ELT best practices.
• Experience working with Azure Data Lake Storage (ADLS) and integrating it with Snowflake.
• Familiarity with monitoring and logging using Azure tools (e.g., Azure Monitor, Log Analytics) and Snowflake query/performance history.
• Experience in building data pipelines for AI/ML use cases (feature stores, model training datasets, inference data flows).
• Familiarity with Azure AI/ML services such as Azure Machine Learning, Cognitive Services, or Azure OpenAI.