Yogesh Verma
Senior Consultant @Celebal Technologies || Databricks | Azure | Spark Streaming | Optimize Enterprise Workload | Data & AI
- Role
- Senior Data Engineer at Celebal Technologies
- Location
- Ajmer, RJ, IN
- LinkedIn followers
- 500 followers
About Yogesh Verma
With 5+ years of hands-on experience as a Data Engineer, I have a strong track record in designing and implementing data solutions. My expertise spans the entire data lifecycle, including architecture design, data extraction, warehousing, transformation, and real-time analytics.I am proficient in using Databricks and Azure Data Factory (ADF) for data integration and processing. I have successfully executed cloud migration projects, transforming legacy systems into scalable cloud-based solutions. My experience includes developing effective data extraction methods from diverse sources and utilizing Spark Structured Streaming for real-time data analysis. & : Spark Structured Streaming, Python, Pandas, EventHub, GCP PUB/SUB Unity Catalog /: MSSQL Server, Azure Synapse Datawarehouse, MySQL, Databricks, GCP Big Query Lakehouse, Postgres SQL, Netezza, Teradata: Azure Databricks, Synapse, EventHub, ADF, SQL, ADLS, Blob Storage, Data Lake’ :Let\'s connect and explore how we can drive success through data-driven strategies together-+91••••••••64 -+91••••••••96
Experience
Senior Data Engineer
Apr 2021 — Present · Jaipur, IN
Creating Azure Data Factory and DLT pipelines (Databricks)• Fetching data from multiple sources like PostgreSQL, PUBSUB, SQL Server, SharePoint, and API\'s• Parsing JSON, XML, and HTML data based on the requirement• Implementing business logic by fetching the data from multiple downstream tables by using PySpark and Spark SQL• Encrypting columns and creating views in Databricks• Finally giving data access to business users through Databricks SQL Warehouse• Creating Azure Data Factory and DLT pipelines (Databricks)• Fetching data from multiple sources like PostgreSQL, PUBSUB, SQL Server, SharePoint, and API\'s • Parsing JSON, XML, and HTML data based on the requirement • Implementing business logic by fetching the data from multiple downstream tables by using PySpark and Spark SQL • Encrypting columns and creating views in Databricks • Finally giving data access to business users through Databricks SQL Warehouse
Education
Government Engineering College, Ajmer
Bachelor of Technology - BTech, Information Technology
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