Jaya Chandra Karthikeyan

Designed and implemented scalable BI solutions using Power BI, Tableau, and QlikView, integrating with Azure SQL, Synapse, and Snowflake to deliver actionable data stories.

Automated data ingestion pipelines with Azure Data Factory, Databricks (PySpark), and Python, unifying data from SAP, Salesforce, and REST APIs.

Built performance-optimized Power BI composite models (DirectQuery/Import), reducing dashboard load times by 50% and enhancing user experience.

Developed real-time KPI dashboards via Power BI Service and created Excel VBA dashboards, decreasing finance team's manual audit effort by 30%.

Implemented robust data governance measures including data masking, sensitivity labeling (Microsoft Purview), and row-level security (RLS) in Power BI for PII/PHI compliance.

Delivered dynamic dashboards in Tableau and Power BI, automating reports that previously required 10+ hours/week manually, boosting reporting efficiency.

Implemented serverless ETL using AWS Glue, Lambda, and Python for lightweight reporting flows, improving data pipeline performance.

Developed predictive models in IBM SPSS for churn risk classification and customer LTV forecasting, enabling data-driven marketing strategies.

Optimized storage usage and reduced AWS costs by implementing S3 lifecycle policies and partitioning strategies for archived analytics data.

Created reusable Python functions to automate file ingestion, cleaning, and formatting across multiple marketing data sources, streamlining data preparation.

Performed comprehensive data analysis and validation through SQL queries, ensuring data accuracy and integrity for reporting.

Utilized Jupyter Notebook for data cleaning, preprocessing, and Google Analytics to generate customized reports and actionable insights.

Executed data wrangling and transformation tasks using Azure Data Factory and Databricks, ensuring high data quality and consistency.

Managed and optimized data storage solutions on Azure (SQL Database, Data Lake, Blob Storage) and Hadoop clusters, enhancing query performance and cost efficiency.

Gathered detailed business requirements through JAD sessions, interviews, and surveys, translating them into technical specifications for development.