Data Analyst · Cloud Analytics · USA

Bharath
Chandra.

I turn complex data into clear decisions—building cloud analytics pipelines, BI systems, and predictive models for financial services, insurance, retail, and telecom.

View my impact
Indiana Wesleyan University · 2025View all photos ↓

01 / EXPERTISE

From raw signals to decisions that move the business.

Cloud-focused analysis backed by strong SQL, scalable data engineering, and executive-ready storytelling.

40%faster ETL processing
60%faster report generation
30%improvement in fraud detection accuracy
30%improvement in SLA adherence
01

Business intelligence

Interactive dashboards and KPI systems that make complex performance visible to senior stakeholders.

TableauPower BICognosDAXExcel
02

Data engineering

Reliable ETL workflows and dimensional models for high-volume, structured and semi-structured data.

SQLInformaticaSSISAlteryxSpark
03

Cloud analytics

Modern analytical platforms spanning migration, storage, transformation, and optimized retrieval.

AWSAzureRedshiftS3Snowflake
04

Predictive analysis

Exploratory analysis, feature engineering, and model-led insight for risk and operational decisions.

PythonREDADatabricksA/B testing

02 / EXPERIENCE

Enterprise analytics, built to perform in the real world.

AVR IT Solutions

FEB 2024 — PRESENT

Data Analyst

  • Engineered SQL queries, stored procedures, and views across Oracle and SQL Server for high-volume telecom and financial data from 10+ source systems.
  • Led an AWS migration from on-premises data warehouses to Redshift and S3, improving analytical retrieval performance while reducing infrastructure cost.
  • Automated Kibana and Python alerting for real-time API log monitoring and order-flow visibility, improving SLA adherence by 30%.
  • Built stakeholder dashboards and ETL pipelines with Tableau, Power BI, Cognos, Informatica, and Apache Spark.

ACI INFOTECH

JUN 2022 — DEC 2023

Junior Data Analyst

  • Designed Tableau and Power BI dashboards for sales, customer behavior, and dealer analytics, reducing reporting cycle time by 25%.
  • Built SSIS packages and Alteryx workflows feeding star and snowflake schemas across the enterprise data warehouse lifecycle.
  • Established data governance practices spanning metadata, data quality, master data, and GDPR/HIPAA compliance standards.
  • Integrated Databricks and Snowflake with Python for big-data analysis, feature engineering, predictive analytics, and A/B test interpretation using R.

03 / LEARNING

Technical depth, with leadership perspective.

EDUCATION

Master’s in Information Technology Leadership

Indiana Wesleyan University · 2024–2025

CERTIFICATION

Data Analytics

Cisco Networking Academy

04 / MILESTONE

A milestone built on learning, persistence, and growth.

Master’s in Information Technology Leadership

Indiana Wesleyan University · Class of 2025

Completing my Master’s in Information Technology Leadership marked an important step in my professional journey, strengthening the connection between technology, data, leadership, and business decision-making.

The experience expanded my perspective beyond technical execution—helping me understand how data and technology can be used to solve business problems, support teams, and drive meaningful decisions.

Barnes Student Center
Master’s graduation celebration
Indiana Wesleyan University · 2025
Master of Science · Information Technology Leadership Indiana Wesleyan University

05 / PROJECTS

Applied work, documented end to end.

Open the case study to see the challenge, technical workflow, contribution, and measurable outcome.

PROJECT 01 / CASE STUDY

Cloud Data Analytics & SLA Monitoring

Enterprise Analytics · Cloud Migration · Data Engineering · Business Intelligence

SQL · Oracle · SQL Server · AWS · Redshift · S3 · Python · Kibana · Tableau · Power BI · Cognos · Informatica · Apache Spark

Overview

An enterprise analytics initiative focused on transforming high-volume telecom and financial data, modernizing analytical infrastructure through AWS, improving operational monitoring, and delivering business intelligence to stakeholders.

My work spanned SQL development, cloud data migration, automated monitoring, ETL development, and dashboard reporting across more than 10 source systems.

The Challenge

The analytics environment involved high-volume data originating from more than 10 source systems and required reliable transformation, integration, monitoring, and reporting.

The work required supporting analytical workloads across multiple database and reporting technologies while improving the visibility of API and order-flow operations.

What I Did

Data Transformation

Engineered complex SQL queries, stored procedures, and database views in Oracle and SQL Server to transform and prepare telecom and financial data for downstream analytical workloads.

Cloud Analytics

Contributed to the migration of on-premises data warehouse workloads to AWS using Amazon Redshift and Amazon S3.

The cloud environment supported analytical data storage and retrieval while helping modernize the existing analytics infrastructure.

Operational Monitoring & Automation

Developed automated alerting and reporting workflows using Python and Kibana for real-time API log monitoring and order-flow visibility.

This work contributed to a reported 30% improvement in SLA adherence by helping operational teams identify and respond to issues more effectively.

ETL & Data Processing

Designed ETL workflows using Informatica and Apache Spark to process structured and semi-structured data from multiple sources.

The resulting datasets supported downstream analytics and business reporting.

Business Intelligence

Built interactive dashboards and reporting solutions using Tableau, Power BI, and Cognos for senior stakeholders.

These reporting solutions helped transform processed enterprise data into accessible business information for analysis and decision-making.

Analytics Workflow

01
10+ Source Systems
02
Oracle + SQL ServerSQL Queries · Stored Procedures · Views
03
ETL & ProcessingInformatica · Apache Spark
04
AWS Cloud AnalyticsAmazon S3 · Amazon Redshift
05
Monitoring & AutomationPython · Kibana
06
Business IntelligenceTableau · Power BI · Cognos
07
Stakeholder Decision Support

Technologies

Databases
Oracle · SQL Server · Amazon Redshift
Cloud
AWS · Amazon S3 · Amazon Redshift
Data Engineering
SQL · Informatica · Apache Spark
Automation & Monitoring
Python · Kibana
Business Intelligence
Tableau · Power BI · Cognos

Key Outcome

The project combined data engineering, cloud analytics, monitoring automation, and business intelligence into an enterprise analytics workflow.

A measurable outcome I can attribute to this work is a 30% improvement in SLA adherence associated with automated API log monitoring and order-flow visibility.

What This Project Demonstrates

This case study demonstrates practical experience across:

  • Advanced SQL development
  • Enterprise data transformation
  • ETL pipeline development
  • AWS cloud analytics
  • Data warehouse migration
  • Python automation
  • API and operational monitoring
  • High-volume data processing
  • Business intelligence development
  • Stakeholder reporting

Confidentiality

This case study summarizes professional experience at a high level. Proprietary datasets, source code, internal dashboards, system architecture, client information, and other confidential materials are intentionally excluded.

Let’s make your data easier to act on.

I’m open to conversations about data analytics, BI development, and cloud data opportunities.

Call (484) 252-8785 Download resume ↓