Kaviya

Mahendran

Hi, I’m Kaviya Mahendran, a Data Analyst with experience in SQL, Python, and Power BI, focused on turning complex data into clear, actionable insights. I work on real world analytics problems including data governance, ETL pipelines, retention analysis, and stakeholder ready dashboards.

Data Analyst with experience in SQL, Python, Power BI, & dashboard automation.

I solve business questions with data, automate workflows, and build visual insights that decision makers use daily.

"Data for decisions"

Industry Experience

Charity, CRM analytics, fundraising performance, operations reporting, non profit environments

Business & Analytics Methods

Campaign performance analysis, segmentation, forecasting support, data governance, stakeholder requirements gathering

Databases & Platforms

Relational databases, CRM systems (Donorflex, Donorfy), cloud based data sources, production datasets

Data Visualisation & Reporting

Power BI (DAX, data modelling), Tableau, dashboard design, KPI tracking, stakeholder ready reporting

Data Analysis & Modelling

SQL (complex joins, CTEs), Python (Pandas, NumPy), Excel (Power Query, advanced formulas), data cleaning, data validation, exploratory analysis, A/B testing

Data Engineering & Automation

ETL processes, API data extraction, Python automation scripts, CRM data migrations, scheduled reporting

AREAS OF INTEREST

PORTFOLIO

A/B Campaign Performance Analysis Evaluating campaign effectiveness using controlled experiments Problem: Marketing campaigns lacked clear evidence on which variants drove higher engagement and conversions. Approach: Designed A/B tests, cleaned campaign data, and analysed uplift using statistical comparison techniques. Tools: Python (Pandas), SQL, Excel Impact: Identified higher performing variants and delivered data driven recommendations for future campaigns.

An abstract image of a cube in blue and purple

DATA GOVERNANCE TOOLKIT Standardised data quality rules and reduced recurring issues across datasets. Problem: Inconsistent data definitions, missing validations, and unclear ownership made reporting unreliable and increased the risk of data quality issues. Approach: Developed a data governance toolkit to define data standards, validation rules, and quality checks across datasets, with clear documentation for usage and ownership. Tools: Python, SQL, Excel Impact: Improved data consistency, reduced recurring data issues, and increased confidence in analytical outputs.

a computer screen with a bunch of code on it

ETL Pipeline for Automated Reporting Building a repeatable pipeline for reliable business reporting Problem: Data required for recurring reports was sourced from multiple systems and processed manually, leading to delays and inconsistencies. Approach: Built an end to end ETL pipeline to extract data from APIs and databases, transform it using Python, and load curated datasets for reporting. Tools: Python, SQL, APIs Impact: Reduced manual reporting effort, improved data consistency, and enabled reliable scheduled reporting.

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CRM MIGRATION SIMULATOR Ensuring data accuracy after a CRM system migration Problem: Post migration donor data from Donorflex to Donorfy required validation to identify data loss, mismatches, and orphaned records. Approach: Built SQL validation queries to compare pre and post migration records, flag inconsistencies, and automate integrity checks. Tools: SQL, Python Impact: Identified critical data discrepancies, reduced data errors by ~15%, and automated recurring validation checks.

Fundraising Performance Dashboard Providing stakeholders with real time visibility into fundraising metrics Problem: Stakeholders relied on manual reports and lacked a single source of truth for fundraising performance. Approach: Built a Power BI dashboard with cleaned data models, KPIs, and interactive filters for ongoing reporting. Tools: Power BI (DAX), Excel, SQL Impact: Reduced manual reporting effort and enabled faster, self serve access to performance insights.

DONOR RETENTION ANALYSIS Understanding donor behaviour to support retention strategies Problem: The organisation lacked visibility into donor retention trends and factors influencing repeat donations. Approach: Analysed historical donation data to calculate retention rates, segment donors by behaviour, and identify drivers of churn and loyalty. Tools: Python (Pandas), SQL, Excel Impact: Identified key retention patterns and provided insights to support targeted donor engagement strategies.

DATA LAKE SIMULATOR Simulating scalable ingestion and storage of diverse datasets Problem: Traditional data storage approaches struggled to handle growing volumes of diverse data sources efficiently. Approach: Built a data lake simulator to ingest raw data from multiple sources, store it in a centralised structure, and enable downstream analysis. Tools: Python, SQL, file based storage Impact: Demonstrated scalable data ingestion patterns and supported analytics on large, diverse datasets.

LEAVE A MESSAGE

YOU CAN FIND ME ON

@elsy_kaviya_

https://bit.ly/4pzDq10

@kaaviya_mahendra

https://github.com/Kaviya-Mahendran

linkedin.com/in/kaaviya-mahendran

kaviyamahen@gmail.com

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