Azure Data Engineer

Role: Azure Data Engineer

Location: Columbus, OH

Please share resumes to

pgo AT THE RATE OF Comtechglobal DOT COM

Must-Have:

• General Knowledge of Databricks, Jupyter Notebooks, Synapse Notebooks, or comparable code-based application (Python, PySpark, or R).
  • Familiar with basic Linear Regression, Statistical Models, and ODBC connections from said application
    • Linear Regression for basic understanding of forecasting models
    • Statistical Models for more advanced forecasting and prediction models
    • ODBC connections for moving data in and out of the application (when using code based applications, it's rare to have multiple native connectors, so ODBC will be needed for some of the connections between Snowflake, Synapse, Storage, PBI, etc )
• Experience working in Azure and navigating Azure's access policies
  • Understand Subscriptions -> Resource Groups -> Resource permissions
  • Understand basic connection properties to different storage accounts (Does not need to know all of these, but at least one for each resource).
    • Azure Storage Containers (use SAS Tokens, Access Keys, and Azure AD)
    • Databricks (use Hive storage tables, Basic Auth, API keys, and Azure AD)
    • Synapse (use external storage account (ADLS), internal SQL Pool, and Azure AD)
    • PowerBI (use rest api for dataset queries, service principals, DAX, and Azure AD)
• Experience automating tasks and creating automation applications of some kind
  • Basics of any good automation application:
    • Utilizes "Try" methodology frequently, which allows for error handling and retry methods
    • Maintains logging somewhere, and can write the outputs of the logs when needed
    • Operates on a zero-trust policy, meaning it maintains it's own credentials and access to the resources it connects to

Nice to Have:

• Experience working with Power BI Premium, building Power BI reports, and working with AAS models
• Experience writing SQL queries (Maybe a 'Must-Have' item, but I suppose we could supply a Jr resource with files if they couldn't write SQL)
• Experience with Azure ML Studio
  • ARFF files (Attribute-Relation File Formats)
  • Score Models
  • Saving visualizations from Azure ML as PNG files
  • Text Analytics
  • Incorporating 'Vowpal Wabbit' modules
• Experience using REST Api calls to parse and gather data. Then translating that data into tabular outputs
• Experience with Azure governance and security roles (contributor, reader, owner, etc..)
  • This is good because Azure ML studio can run as a managed identity with other resources, but you need to configure proper permissions

Skills :