Expedite Technology Solutions LLC
Data Engineer - Snowflake and Databricks
Expedite Technology Solutions LLC, Seattle, Washington, us, 98127
Primary skill:
• DBT Labs
• Snowflake
Secondary skill:
• Databricks
• Databricks Unity Catalog
Responsibilities: • Develop, optimize, and maintain data pipelines using Azure Data Factory (ADF), DBT Labs, Snowflake, and Databricks. • Develop reusable jobs and configuration-based integration framework to optimize development and scalability. • Manage data ingestion for structured and unstructured data ( landing/ lake house: ADLS, Sources: DLS, Salesforce, SharePoint Documents Libraries, Partner Data: Client, IHME, WASDE etc. ). • Implement and optimize ELT processes, source-to-target mapping, and transformation logic in DBT Labs, Azure Data Factory, Databricks Notebook, Snow SQL etc. • Collaborate with data scientists, analysts, data engineers, report developers and infrastructure engineers for end-to-end support. • Co-develop CI/CD best practices, automation, and pipelines with Infrastructure engineers for code deployments using GitHub Actions. • Bring in automation from source-to-target mappings to data pipelines and data lineage in Collibra. Required Experience • Hands-on experience building pipelines with ADF, Snowflake, Databricks, and DBT Labs. • Expertise in Azure Cloud with Databricks, Snowflake, and ADLS Gen2 integration. • Data Warehousing and Lakehouse Knowledge: Proficient with ELT processes, *** Tables, and External Tables for structured/unstructured data. • Experience with Databricks Unity Catalog and data sharing technologies. • Strong skills in CI/CD (Azure DevOps, GitHub Actions) and version control (GitHub). Strong cross-functional collaboration and technical support experience for data scientists, report developers and analysts.
Responsibilities: • Develop, optimize, and maintain data pipelines using Azure Data Factory (ADF), DBT Labs, Snowflake, and Databricks. • Develop reusable jobs and configuration-based integration framework to optimize development and scalability. • Manage data ingestion for structured and unstructured data ( landing/ lake house: ADLS, Sources: DLS, Salesforce, SharePoint Documents Libraries, Partner Data: Client, IHME, WASDE etc. ). • Implement and optimize ELT processes, source-to-target mapping, and transformation logic in DBT Labs, Azure Data Factory, Databricks Notebook, Snow SQL etc. • Collaborate with data scientists, analysts, data engineers, report developers and infrastructure engineers for end-to-end support. • Co-develop CI/CD best practices, automation, and pipelines with Infrastructure engineers for code deployments using GitHub Actions. • Bring in automation from source-to-target mappings to data pipelines and data lineage in Collibra. Required Experience • Hands-on experience building pipelines with ADF, Snowflake, Databricks, and DBT Labs. • Expertise in Azure Cloud with Databricks, Snowflake, and ADLS Gen2 integration. • Data Warehousing and Lakehouse Knowledge: Proficient with ELT processes, *** Tables, and External Tables for structured/unstructured data. • Experience with Databricks Unity Catalog and data sharing technologies. • Strong skills in CI/CD (Azure DevOps, GitHub Actions) and version control (GitHub). Strong cross-functional collaboration and technical support experience for data scientists, report developers and analysts.