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Participate in daily agile and scrum processes to understand changing business requirements, including examining system configurations, operating procedures, and gathering functional requirements. Ingest and prepare business-ready data in the cloud using Azure Data Factory (ADF) to build ELT (Extract, Load, and Transform)/ETL (Extract, Transform, and Load) data pipelines, then move the data into a data warehouse (Dedicated SQL Pool) and create data lake zones for analytics and visualization. Work with Azure Data Factory and Azure Databricks to extract, load, and transform data from cloud sources and on-premises databases such as Oracle, SAP, and SQL Server to Data Lake Storage and Azure Synapse Analytics. Create an Azure Data Factory Pipeline Template to migrate on-premises data platforms to Azure Cloud using batch processing methods via incremental or full loads, and develop an ADF config-driven framework to pull data from multiple sources with different table structures, reducing manual effort and resource usage. Analyze, design, and build modern data solutions using Azure PaaS services to support data visualization. Understand the current production state of applications and assess the impact of new implementations on existing business processes. Enable private endpoints, firewall settings, and Azure Key Vault for robust data security. Analyze existing SSIS packages and integrate them with Azure Data Factory, utilizing SSIS transformations such as Lookup, Derived Column, Data Conversion, Aggregate, Conditional Split, SQL Task, Script Task, and Send Mail Task. Create JSON structures for data storage in Azure Cosmos DB (SQL API), develop stored procedures and functions, and collaborate with the API team to optimize Cosmos DB queries to use fewer request units. Design data models in Snowflake and perform ELT using Snowflake SQL, implementing complex stored procedures and adhering to data warehouse and ETL best practices. Design and customize dimension data models for data warehouses supporting data in Azure Synapse Analytics, selecting appropriate distribution methods for optimized data loading, and implementing complex stored procedures. Build distributed in-memory applications using Spark and perform analytics on large datasets with Python and Spark SQL, including transformation logic in Databricks and mounting/unmounting Azure Blob Storage. Read data from various file formats such as Parquet, Avro, CSV, and JSON using PySpark in Azure Databricks, perform data extraction and transformation, and generate insights into customer usage patterns, inserting curated data into data warehouses. Create data visualization reports and dashboards in Power BI using data from data warehouses, flat files, and Azure SQL. Handle troubleshooting and investigations related to SQL queries, stored procedures, long-running jobs, and Azure service performance issues. Utilize Azure Monitor and Alert services to create monitors, alerts, and notifications for Data Factory, Synapse Analytics, and Data Lake Storage. Support daily GIT operations for various projects, maintain GIT repositories, and manage access control procedures. Create CI/CD pipelines using Azure DevOps to deploy Azure services such as Storage, Data Factory, Key Vault, and Logic Apps via ARM templates. JOB REQUIREMENTS : Master's degree in Computer Science, Computer Information Systems, Engineering, or related fields plus 2 years of experience, or Bachelor's degree plus 5 years of relevant experience. Acceptable experience includes working with Azure Data Factory, Oracle, SQL Server, Azure Databricks, Azure Synapse Analytics, Data Lake Storage, Azure PaaS services, SSIS, Cosmos DB, Python, Spark, Azure SQL, SQL queries, Azure Monitor, GIT, and ARM Templates. HOURS : M-F, 8:00 a.m. 5:00 p.m. JOB LOCATION : Dallas, Texas. Travel may be required, but candidates must be willing to relocate as needed. #J-18808-Ljbffr