Karthikeyan Jayaprakash

• Responsible for creating end-to-end metadata-driven ETL pipelines for various data sources using PySpark in Azure Databricks.

• Implemented role-based access control on Neo4j.

• Automated the build and deployment of data products using Azure DevOps.

• Implemented metadata-driven ETL orchestration in Databricks using Terraform.

• Managed infrastructure using Terraform.

• Created streaming pipelines to process data from Kafka to Neo4j.

• Leveraged Azure services such as Azure Data Factory, Azure Databricks, and Azure DevOps to implement data solutions.

• Designed and developed scalable data processing workflows and ETL pipelines to extract, transform, and load data from diverse sources using SQL and PySpark.

• Automated tasks using Python to eliminate manual activities.

• Built and deployed solutions using Azure DevOps.

• Developed and implemented analytical models for Power BI, accelerating self-service business intelligence and data-informed decision-making processes.

• Developed logic to handle complex transformations and calculations.

• Analyzed business requirements and data sources to identify data modeling needs and opportunities for improvement.

• Developed data models to represent business entities, attributes, relationships, and data flows using SQL and Erwin.

• Participated in data architecture reviews and provided recommendations for optimizing data models and database structures.

• Designed and developed conceptual, logical, and physical data models using SQL and Erwin based on business requirements and industry best practices.

• Collaborated with stakeholders to understand data needs and define data modeling standards and conventions.

• Conducted data model reviews and validations to ensure accuracy, completeness, and consistency.