Length 12 months Need strong experience and expertise with Azure Databricks and DataWarehouse RESPONSIBILITIES Build large-scale batch and real-time data pipelines with data processing frameworks in Azure cloud platform. Designing and implementing highly performant data ingestion pipelines from multiple sources using Azure Databricks. Direct experience of building data pipelines using Azure Data Factory and Databricks. Developing scalable and re-usable frameworks for ingesting of datasets Lead design of ETL, data integration and data migration. Partner with architects, engineers, information analysts, business, and technology stakeholders for developing and deploying enterprise grade platforms that enable data-driven solutions. Integrating the end to end data pipeline - to take data from source systems to target data repositories ensuring the quality and consistency of data is maintained at all times Working with event based / streaming technologies to ingest and process data Working with other members of the project team to support delivery of additional project components (API interfaces, Search) Evaluating the performance and applicability of multiple tools against customer requirements REQUIREMENTS Experience on ADLS, Azure Databricks, Azure SQL DB and Datawarehouse Strong working experience in Implementation of Azure cloud components using Azure Data Factory , Azure Data Analytics, Azure Data Lake, Azure Data Catalogue, LogicApps and FunctionApps Have knowledge in Azure Storage services (ADLS, Storage Accounts) Expertise in designing and deploying data applications on cloud solutions on Azure Hands on experience in performance tuning and optimizing code running in Databricks environment Good understanding of SQL, T-SQL and/or PL/SQL Should have experience working in Agile projects with knowledge in Jira Good to have handled Data Ingestion projects in Azure environment Demonstrated analytical and problem-solving skills particularly those that apply to a big data environment • Experience on Python scripting, Spark SQL PySpark is a plus.