Azure Architect - PySpark/DataLake

Mandatory Skill combination : Azure Synapse, Data Factory, Azure Data Lake Storage, Pyspark Responsibilities : - Develop and maintain scalable architecture, database design and data pipelines and build out new Data Source integrations to support continuing increases in data volume and complexity - Design, develop and maintain the data ingestion and integration frameworks using Azure Cloud Services - Assist in designing end to end data and Analytics solution architecture and perform POCs within Azure - Drive the design, sizing, POC setup, etc.

of Azure environments and related services for the use cases and the solutions - Reviews the solution requirements support architecture design to ensure the selection of appropriate technology, efficient use of resources and integration of multiple systems and technology.

- Must possess good client-facing experience with the ability to facilitate requirements sessions and lead teams - Support internal presentations to technical and business teams - Provide technical guidance, mentoring and code review, design level technical best practices Experience Needed: - 9 -12 years of industry experience and at least 3 years of experience in architect role is required along with at least 3 to 4 years' experience designing and building analytics solutions in Azure.

- Experience in architecting data ingestion/integration frameworks capable of processing structured, semi-structured & unstructured data sets in batch & real-time - Hands-on experience in the design of reporting schemas, data marts and development of reporting solutions - Develop batch processing, streaming and integration solutions and process Structured and Non-Structured Data - Demonstrated experience with ETL development both on-premises and in the cloud using SSIS, Data Factory, and Azure Analysis Services and other ETL technologies.

- Experience in Perform Design, Development & Deployment using Azure Services (Azure Synapse, Data Factory, Azure Data Lake Storage, Python) - Worked with transactional, temporal, time series, and structured and unstructured data.

(ref:hirist.tech)