Anandu Raveendran

Optimized SQL queries (CTEs, joins, window functions) along with Advanced Excel to improve data processing and analysis speed.
Designed and implemented end-to-end data pipelines, extracting transactional data from SQL databases, transforming it into structured datasets, and integrating with Power BI for automated reporting.
Developed and maintained 12+ interactive Power BI dashboards to monitor revenue, customer acquisition, retention rate, operational efficiency, and product performance across multiple business units.
Built advanced DAX measures including YoY growth, MoM comparison, rolling averages, cumulative totals, contribution %, and dynamic KPI switching using slicers to enable flexible executive reporting.
Established a centralized KPI framework by collaborating with product and operations teams to standardize metric definitions, validation rules, and reporting logic, ensuring data consistency across departments.
Conducted Exploratory Data Analysis (EDA) using Python (Pandas, NumPy) to identify customer behavior patterns, seasonality trends, and revenue fluctuations; visualized insights using Matplotlib and Seaborn to support business decisions.
Partnered with product, engineering, and business teams to define KPIs, metrics, and reporting logic, ensuring data consistency across dashboards.

Optimized SQL queries (CTEs, joins, window functions) along with Advanced Excel to improve data processing and analysis speed., Designed and implemented end-to-end data pipelines, extracting transactional data from SQL databases, transforming it into structured datasets, and integrating with Power BI for automated reporting., Developed and maintained 12+ interactive Power BI dashboards to monitor revenue, customer acquisition, retention rate, operational efficiency, and product performance across multiple business units., Built advanced DAX measures including YoY growth, MoM comparison, rolling averages, cumulative totals, contribution %, and dynamic KPI switching using slicers to enable flexible executive reporting., Established a centralized KPI framework by collaborating with product and operations teams to standardize metric definitions, validation rules, and reporting logic, ensuring data consistency across departments., Conducted Exploratory Data Analysis (EDA) using Python (Pandas, NumPy) to identify customer behavior patterns, seasonality trends, and revenue fluctuations; visualized insights using Matplotlib and Seaborn to support business decisions., Partnered with product, engineering, and business teams to define KPIs, metrics, and reporting logic, ensuring data consistency across dashboards.

Extracted and consolidated data from multiple internal systems into structured Excel and SQL datasets for analysis and reporting.
Cleaned and transformed raw datasets by handling missing values, duplicates, inconsistent formats, and data mismatches to improve overall data reliability.
Wrote SQL queries to generate summary tables, aggregated reports, and filtered datasets for business teams.
Supported Power BI development by preparing fact and dimension tables and ensuring proper relationships for optimized data modeling.
Performed data reconciliation between monthly reports and database records to identify mismatches and correct inconsistencies.
Documented data definitions, transformation logic, and reporting workflows for internal knowledge sharing and process continuity.
Improved data quality through validation, reconciliation, and standardization.
Assisted senior analysts with data preparation and documentation, supporting dashboard development and analytics workflows.

Extracted and consolidated data from multiple internal systems into structured Excel and SQL datasets for analysis and reporting., Cleaned and transformed raw datasets by handling missing values, duplicates, inconsistent formats, and data mismatches to improve overall data reliability., Wrote SQL queries to generate summary tables, aggregated reports, and filtered datasets for business teams., Supported Power BI development by preparing fact and dimension tables and ensuring proper relationships for optimized data modeling., Performed data reconciliation between monthly reports and database records to identify mismatches and correct inconsistencies., Documented data definitions, transformation logic, and reporting workflows for internal knowledge sharing and process continuity., Improved data quality through validation, reconciliation, and standardization., Assisted senior analysts with data preparation and documentation, supporting dashboard development and analytics workflows.