BI/Reporting Engineer

• Overall 10 years of experience in Industry including 6+Years of experience as developer using Databricks/Spark Ecosystems.

• Hands on experience on Unified Data Analytics with Databricks, Databricks Workspace User Interface, Managing Databricks Notebooks, Delta Lake with Python, Delta Lake with Spark SQL.

• Good understanding of Spark Architecture with Databricks, Structured Streaming. Setting Microsoft Azure with Databricks, Databricks Workspace for Business Analytics, Manage Clusters In Databricks, Managing the Machine Learning Lifecycle

• Hands on experience Data extraction(extract, Schemas, corrupt record handling and parallelized code), transformations and loads (user - defined functions, join optimizations) and Production (optimize and automate Extract, Transform and Load)

• Spark Data Frame API
• Python for Data Science
• Spark Programming
• SQL for Data Analysis
• Simplify Data analysis With Python
• Manage Clusters Databricks
• Databrick Administration
• Data Extraction and Transformation and Load
• Implementing Partitioning and Programming with MapReduce
• Setting up Azure Databricks Account
• Linux Command

• Experience in developing Spark applications using Spark-SQL in Databricks for data extraction, transformation, and aggregation from multiple file formats for Analyzing & transforming the data to uncover insights into the customer usage patterns.
• Extract Transform and Load data from sources Systems to Azure Data Storage services using a combination of Azure Data factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics. Data ingestion to one or more Azure services (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in Azure Databricks
• Develop Spark applications using pyspark and spark SQL for data extraction, transformation, and aggregation from multiple file formats for analyzing and transforming the data uncover insight into the customer usage patterns
• Hands on experience on developing SQL Scripts for automation
• Responsible for estimating the cluster size, monitoring, and troubleshooting of the Spark databricks cluster
• Ability to apply the spark DataFrame API to complete Data manipulation within spark session
• Good understanding of Spark Architecture including spark core, spark SQL, DataFrame, Spark streaming, Driver Node, Worker Node, Stages, Executors and Tasks, Deployment modes, the Execution hierarchy, fault tolerance, and collection
• Collaborate with delivery leadership to deliver projects on time adhering to the quality standards
• Contribute to the growth of the Microsoft Azure practice by helping with solutioning for prospects
• Problem-solving skills along with good interpersonal & communication skills
• Self-starter who can pick up any other relevant Azure Services in the Analytics space

Arrow Electronics