Proven work history with Dedicated SQL Pools.
Microsoft Azure Synapse Analytics experience is essential. (Dedicated SQL Pool, Azure Data Factory, Azure Storage)
Design and implement end-to-end data solutions using Azure Synapse.
Will likely have a degree in Computer Science, Statistics, Informatics, Information Technology or quantitative field.
Excellent communication skills with the ability to work within a team, across the business, and build strong relationships.
End-to-end Data Warehouse experience: ingestion, ETL, big data pipelines, data architecture, message queuing, stream processing, BI/Reporting, and Data Security.
Ability to build processes supporting data transformation, data structures, metadata, dependency, and workload management, as well as the ability to manipulate, process, and extract value from large, disconnected structured and unstructured datasets.
Advanced SQL/relational database knowledge (including SSIS), query authoring (SQL).
Experience performing root cause analysis on data, answering specific business questions, and identifying opportunities for improvement.
Understanding of machine learning and artificial intelligence ML libraries and frameworks (TensorFlow, Spark, etc).
Experience with Data Governance (Quality, Lineage, Data dictionary, and Security).
Proficiency in programming language such as Python or equivalent for data engineering tasks.
Familiarity with Git for version control and Azure DevOps for continuous integration and continuous deployment (CI/CD) processes.

External Skills And Expertise

Microsoft Azure Synapse Analytics experience is essential. (Dedicated SQL Pool, Azure Data Factory, Azure Storage)
Excellent communication skills with the ability to work within a team, across the business, and build strong relationships.
End-to-end Data Warehouse experience: ingestion, ETL, big data pipelines, data architecture message queuing, stream processing, BI/Reporting, and Data Security.
Ability to build processes supporting data transformation, data structures, metadata, dependency, and workload management, as well as the ability to manipulate, process, and extract value from large, disconnected structured and unstructured datasets.
Advanced SQL/relational database knowledge (including SSIS), query authoring (SQL).
Experience performing root cause analysis on data, answering specific business questions, and identifying opportunities for improvement.
Understanding of machine learning and artificial intelligence ML libraries and frameworks (TensorFlow, Spark, etc).
Experience with Data Governance (Quality, Lineage, Data dictionary, and Security).
Proficiency in programming language such as Python or equivalent for data engineering tasks.
Familiarity with Git for version control and Azure DevOps for continuous integration and continuous deployment (CI/CD) processes.

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