Data Analyst Power Bi

SRAVANI ELURI

Gainesville, FL +1-475-***-**** ****************@*****.*** LinkedIn GitHub

PROFESSIONAL SUMMARY

Data Analyst with 2+ years of experience leveraging SQL, Python, and advanced Excel for data wrangling, ETL, and statistical analysis. Proficient in Power BI and Tableau for interactive dashboards, KPI tracking, and business intelligence reporting. Skilled in predictive modeling, data visualization, and translating complex datasets into actionable insights that support decision-making across stakeholders. Experienced in cloud data platforms (AWS, Azure, GCP) to deliver scalable analytics solutions. TECHNICAL SKILLS

Programming & Analytics: Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), R, SQL, PySpark. Data Visualization & BI: Power BI, Tableau, Excel (Power Query, Pivot Tables, DAX), Looker. Databases & Warehousing: MySQL, PostgreSQL, Oracle, Snowflake, Amazon Redshift, Google BigQuery. ETL & Data Processing: Apache Airflow, AWS Glue, Azure Data Factory, Spark. Cloud Analytics Platforms: AWS (S3, RDS, Redshift, QuickSight), Azure (Data Factory, Synapse Analytics), GCP (BigQuery, Looker Studio).

Data Management & Tools: Data Modeling, Data Wrangling, Data Cleaning, SQL Optimization. Statistics & Machine Learning: Hypothesis Testing, Regression Analysis, Classification, Clustering, Time Series Forecasting. Collaboration & Version Control: Git, Jira, Confluence. PROFESSIONAL EXPERIENCE

Data Analyst Jan 2024 – Present

Amalgam Technologies Texas

• Built automated ETL workflows with AWS Glue and Azure Data Factory that delivered fresh data to Redshift, cutting reporting delays by 40% and supporting real-time dashboards.

• Improved efficiency of business reporting by optimizing Snowflake SQL queries and designing scalable data models, which shortened query times by 35% on high-volume datasets.

• Presented business leaders with interactive dashboards in Power BI and Tableau, giving clear visibility into KPIs and driving a 30% improvement in decision-making accuracy.

• Cleaned and transformed raw data using Python libraries (Pandas, NumPy), raising dataset reliability and producing forecasting models with 22% higher accuracy.

• Automated recurring reporting processes by developing Python Boto3 scripts and AWS CLI tasks, freeing analysts from 15+ hours of manual effort each week.

• Partnered with operations and finance teams to define KPIs, then translated them into actionable insights through dashboards and reports, which improved adoption of analytics solutions.

• Reduced infrastructure costs by applying S3 lifecycle rules and Redshift performance tuning, leading to a consistent 18% decrease in monthly cloud spend.

• Applied predictive modeling in Scikit-learn to sales and demand data, enabling leadership to make inventory and planning decisions backed by measurable data trends. Data Platform Engineer Jan 2023 – May 2023

Sacred Heart University CT

• Automated research application deployments through Jenkins and GitHub Actions, bringing release cycles down from multiple hours to less than 30 minutes for faculty users.

• Ensured uninterrupted access to academic data platforms by containerizing workloads with Docker and orchestrating them on Kubernetes, sustaining 99.9% system uptime.

• Introduced real-time monitoring with Prometheus and Grafana, which improved anomaly detection by 40% and minimized disruptions in ongoing research projects.

• Reduced infrastructure costs by 30% after re-engineering scheduled batch jobs into AWS Lambda and GCP Functions, removing the need for idle server provisioning.

• Accelerated experimentation by developing reproducible cloud environments with Terraform, which eliminated configuration drift and simplified research onboarding.

• Strengthened reliability of analytics pipelines by embedding SonarQube checks into CI/CD workflows, lowering code rollback incidents by 20%.

• Streamlined faculty support by centralizing logs within the ELK Stack, enabling cross-team debugging and decreasing mean time to resolution on issues by 45%.

• Supported AI-driven academic research by designing scalable multi-cloud data pipelines, ensuring reproducible workflows that advanced large-scale analytics studies. Data Visualization Analyst Jan 2022 – Jun 2022

Cognizant Hyderabad, India

• Built interactive Tableau dashboards for financial operations that unified disparate data feeds, enabling leadership to cut reporting cycles from days to hours.

• Used Power BI with complex DAX measures to highlight real-time sales performance, which helped regional managers act on underperforming markets 30% faster.

• Streamlined SQL data pipelines to prepare healthcare claims data for visualization, raising accuracy of regulatory compliance dashboards by 25%.

• Automated Excel-based reports into Power Query models, reducing manual rework and freeing analysts from 20 hours of repetitive effort every week.

• Collaborated with product teams to define KPIs and delivered retail analytics dashboards with drill-down insights, leading to a 15% increase in adoption across departments.

• Connected AWS Redshift data sources with Tableau, scaling dashboards to handle terabyte-scale datasets while maintaining sub-second query responses.

• Deployed Python scripts to validate data flowing into dashboards, cutting mismatches with source systems by nearly a third and boosting user trust.

• Redesigned dashboard layouts based on stakeholder feedback and usability testing, which improved navigation and lifted engagement scores by 20% in quarterly reviews. PROJECTS

Customer Churn Analysis

• Queried and prepared 100K+ telecom customer records in SQL and Python (Pandas, NumPy), cleaning inconsistencies in billing, call usage, and complaints to extract churn drivers.

• Built predictive churn models using Logistic Regression and Decision Trees in scikit-learn, validating results with cross- validation and achieving 82% forecasting accuracy.

• Designed Power BI dashboards with churn heatmaps, KPIs, and drill-down filters, helping retention teams prioritize at-risk customers with clear actionable insights.

Real-Time IoT Fleet Monitoring

• Engineered a Kafka-based streaming pipeline to capture GPS and IoT sensor data from 100+ vehicles with structured ingestion for downstream analysis.

• Applied Spark Streaming for near real-time event processing under 5s latency and containerized services on Kubernetes to ensure scalability and fault tolerance.

• Delivered Grafana dashboards combining fuel efficiency, route optimization, and driver behavior metrics, allowing managers to enhance fleet utilization and safety.

Customer Segmentation & Fraud Detection

• Processed 250K+ financial transactions with Python, SQL, and Pandas to flag anomalies and generate customer segments based on spending patterns.

• Leveraged K-Means clustering and Decision Tree models to refine segmentation and fraud detection, boosting risk identification rates by 15%.

• Created Tableau and Power BI dashboards with anomaly trendlines, fraud-risk scores, and segment insights, enabling business teams to act on high-risk accounts quickly. Campus Marketplace

• Designed a university marketplace platform with buyer–seller modules, integrating Stripe API for secure transactions and detailed purchase tracking.

• Automated deployment on AWS ECS using Terraform to maintain elasticity for 200+ concurrent users, while optimizing PostgreSQL + Spring Boot queries to sub-100ms response times.

• Integrated real-time analytics dashboards tracking user retention, purchase trends, and engagement, enabling data- driven enhancements to marketplace growth.

EDUCATION

Masters in Computer and Information Science Aug 2022 - Dec 2023 Sacred Heart University Fairfield, CT

CERTIFICATIONS

• Microsoft Azure Administrator Associate

• Power BI Data Analyst Associate

• Python Data Structures – Coursera

• Excel Skills for Data Analysis – Coursera

• Data Visualization with Tableau – LinkedIn Learning

Contact this candidate