Job Description

We are hiring Data Engineer - Azure Exp- 4 -8 Years Location- Pune/Jaipur/Noida/Gurgaon Notice Period- Immediate Joiner/30 Days/Serving NP - Must Have: Hands on experience with distributed computing framework like DataBricks, Spark-Ecosystem (Spark Core, PySpark, Spark Streaming, SparkSQL) Willing to work with product teams to best optimize product features/functions. Experience on Batch workloads and real time streaming with high volume data frequency Performance optimization on Spark workloads Environment setup, user management, Authentication and cluster management on Databricks Professional curiosity and the ability to enable yourself in new technologies and tasks. Good understanding of SQL and a good grasp of relational and analytical database management theory and practice. Key Skills: Python, SQL and Pyspark Big Data Ecosystem (Hadoop, Hive, Sqoop, HDFS, Hbase) Spark Ecosystem (Spark Core, Spark Streaming, Spark SQL) / Databricks Azure (ADF, ADB, Logic Apps, Azure SQL database, Azure Key Vaults, ADLS, Synapse) AWS (Lambda,AWS Glue, S3, Redshift) Data Modelling, ETL Methodology. Responsibilities: Defines, designs, develops and test software components/applications using Microsoft Azure- (Azure Databricks, Azure Data Factory, Azure Data Lake Storage, Logic Apps, Azure SQL database, Azure Key Vaults, ADLS) Strong SQL skills with experience. Experience handling Structured and unstructured datasets. Experience in Data Modeling and Advanced SQL techniques Experience implementing Azure Data Factory, Airflow, AWS Glue or any other data orchestration tool using latest technologies and techniques. Good exposure in Application Development. The candidate should work independently with minimal supervision. Good To Have: Hands on experience with distributed computing framework like DataBricks. Experience with Databricks migration from On-premise to Cloud OR Cloud to Cloud Migration of ETL workloads from Apache Spark implementations to Databricks Experience on Databricks ML will be a plus Migration from Spark 2.0 to Spark 3.0

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