Senior Data Analyst & Engineer with Cloud & BI Expertise

RUJULA MALINENI

LinkedIn +1-470-***-**** ****************@*****.***

PROFESSIONAL SUMMARY

Certified Data Analyst with 4+ years of experience in data engineering, business analysis, and analytics-driven decision- making across finance, logistics, healthcare, and higher education domains. Skilled in SQL, Python, PySpark, Power BI, Tableau, and cloud platforms with proven expertise in ETL/ELT pipeline development, data warehousing, and dashboard reporting. Strong track record of enabling predictive analytics, KPI dashboards, customer segmentation, and process automation that improved operational efficiency by up to 40%. Collaborative communicator experienced in Agile environments, stakeholder engagement, and cross-functional project leadership. TECHNICAL EXPERIENCE

Programming & Analytics: SQL, Python, PySpark, R, Statistical Modeling, A/B Testing, Forecasting. Data Engineering & Tools : ETL/ELT, Azure Data Factory, SSIS, SSRS, Snowflake, Databricks, Big Query, Hive, Airflow, APIs, Streaming Data, Git, Jira, Azure DevOps, Jenkins, CI/CD for Data Pipelines, Version Control. Databases & Cloud: Azure SQL, AWS Redshift, MySQL, MS SQL Server, MongoDB, Data Lakes, Data Warehousing. Visualization & Reporting: Power BI, Tableau, Excel (VBA, Pivot Tables), Executive Dashboards, KPI Reporting. Data Governance & Security: Data Quality, Data Validation, Data Lineage, Role-Based Access Control, Compliance. Soft Skills: Agile/Scrum, Stakeholder Communication, Cross-functional Collaboration, Process Optimization. WORK EXPERIENCE

Data Engineer Remote, US

Synovatic Inc February 2025 - Present

• Working on USPS Package Tracking and Customer Usage Analysis project to streamline delivery insights, optimize service performance, and enhance decision-making for postal operations.

• Spearheaded data analysis initiatives to track USPS package delivery, customer usage patterns, and service frequency trends.

• Conducted market research using customer usage data to optimize digital marketing efforts, improving campaign ROI through segmentation analysis.

• Translated customer data insights from USPS tracking systems into strategic recommendations for campaign improvements and service segmentation, enhancing data-driven marketing initiatives.

• Analyzed 10M+ package delivery records to uncover trends in customer preferences across service types including First-Class, Priority Mail, and Parcel Select.

• Conducted in-depth analysis to identify package type preferences and service adoption rates, supporting strategic decision- making for service improvement.

• Designed and deployed data lake and data warehouse solutions to centralize USPS tracking data for analytics.

• Automated ETL/ELT workflows using ADF and SQL to improve data ingestion efficiency.

• Applied data validation and quality frameworks, ensuring >98% accuracy in executive reports.

• Collaborated with cross-functional teams to align USPS analytics with business KPIs.

• Applied strong SQL and relational database expertise to structure customer and package-level data into dashboards used for marketing planning.

• Initiated and led process improvements in reporting and dashboard delivery cycles using agile principles.

• Conducted frequency analysis to identify peak usage trends, preferred services by customer segment, and performance by zip code supporting strategic decision-making and product planning.

• Delivered concise and leadership-ready documents for executive reviews, including performance summaries of delivery zones and user behavior led to a 15% increase in operational efficiency.

• Worked with Git for version control, managing SQL script changes across different stages of the project lifecycle. Software Data Engineer Atlanta, GA

Zenspace IT July 2023 – February 2025

• Worked on the Supply Chain Data Reporting Automation to streamline and optimize reporting operations for Financial Loan company.

• Built automated ETL pipelines that integrated real-time financial and campaign performance data into marketing dashboards, supporting agile campaign decision-making to reduce manual report generation time by 40%.

• Leveraged Power BI and SQL to visualize customer data usage trends, enabling real-time optimization of offer targeting and marketing spend while reducing decision turnaround time by 30%.

• Used PySpark to extract trends from relational databases, enabling deeper understanding of customer engagement.

• Created data quality frameworks, addressing data integrity and quality issues to enhance reporting accuracy by 25% during financial analysis.

• Designed and delivered analytic techniques strategically aligned with business goals and data requirements, driving a 20% improvement in stakeholder satisfaction through better data accessibility and visualization.

• Implemented data pipeline orchestration using Airflow, enabling automated monitoring and alerts.

• Built customer segmentation models in Python to support targeted marketing campaigns.

• Partnered with finance teams to deliver cost optimization insights on loan processing.

• Created and tracked KPIs that monitored performance metrics, enabling informed decision-making across business intelligence departments. Developed predictive models and statistical forecasts, improving decision turnaround times.

• Partnered with marketing stakeholders to align campaign metrics with backend data infrastructure, ensuring consistency in marketing performance reporting.

• Independently led marketing workflow reporting enhancements, identifying pain points and proposing incremental changes with minimal supervision.

• Created documentation and delivered user training on marketing analytics tools for business stakeholders, facilitating smooth technology adoption.

• Migrated 15+ on-premises databases to Azure SQL using SSIS, ADF, ensuring zero downtime during the transition.

• Built and optimized data pipelines to integrate real-time data from legacy systems into Azure Cloud, leveraging Jira for project tracking, Hive for querying big data, and working with large-scale big data systems. Senior Data Business Analyst Atlanta, GA

Georgia State University August 2021 – December 2022

• Worked on the High-Performance Computing Market Research Insights Project to optimize marketing strategies and resource allocation.

• Analyzed datasets above 5 million records using PySpark to identify trends, optimize marketing strategies for improved operational efficiency.

• Acted as the go-to resource for marketing strategy data requests, translating complex analyses into executive-level visuals and documents.

• Partnered with business teams to translate analytical findings into strategic recommendations, leading to a 15% increase in operational efficiency.

• Collaborated with marketing teams at Georgia State University to build dashboards that tracked digital campaign performance, helping optimize marketing spend and improve engagement metrics.

• Engineered and fine-tuned over 30 PySpark jobs to process datasets exceeding 10TB, enhancing processing efficiency by 50%.

• Designed a centralized marketing data warehouse integrating campaign, student, and engagement data.

• Created executive dashboards with KPIs, OKRs, and predictive analytics, and driving strategy alignment.

• Defined data governance policies for research datasets, ensuring compliance with HIPAA standards.

• Conducted text mining and NLP analysis on marketing content to optimize engagement.

• Built Power BI dashboards that visualized market insights, that transformed complex datasets into actionable insights enabling real-time decision-making across multiple teams.

• Worked on the Bio informatics, Drug Protein Structural Analysis Project to enhance therapeutic safety using machine learning models.

• Engineered machine learning models (Random Forest and SVM) to classify protein structures for improved drug formulation.

• Processed and analyzed large-scale protein alignment data using Python and PySpark, contributing to drug efficacy studies. Also built dashboards to visualize protein alignment, drug safety metrics, streamlining research workflows.

• Increased accuracy in classifying protein structures by 95%, supporting safer and more effective drug formulations in health care analytics.

• Accelerated the research process by 40%, reducing time to analyze & evaluate protein structures, leading to faster drug development cycles.

Software Data Engineer Hyderabad, India

Infosys Pvt Ltd November 2020 – July 2021

• Worked on the Insurance Claims Risk Management Project for Allstate to improve fraud detection and optimize risk assessment processes.

• Worked closely with stakeholders in marketing and risk departments to tailor reporting on customer claims behavior, informing campaign messaging strategies.

• Created and maintained SQL queries that pulled and validated marketing-relevant customer data across insurance services.

• Designed Power BI dashboards that synthesize large volumes of claims data for use in digital marketing analysis and fraud prevention targeting.

• Engineered risk scoring models for insurance claims, integrating fraud detection algorithms.

• Implemented CI/CD pipelines for SQL scripts and reporting workflows using Azure DevOps.

• Ensured regulatory compliance (GDPR, SOX) in claims reporting and marketing analytics.

• Optimized cloud usage during migration from on-prem to Azure SQL.

• Communicated insights clearly into both technical and non-technical leadership, supporting risk-based marketing and underwriting decisions.

• Ensured marketing report accuracy and timeliness while managing ambiguity across departments and inconsistent data sources.

• Designed and implemented Power BI and Excel, pivot tables, lookups, VBA with dashboards to track and visualize claims trends.

• Delivered in-depth analysis on insurance claims trends, providing insights that reduced fraud-related costs by 20%. PROJECT EXPERIENCE

Title: Music Genre Classification System

Role: Machine Learning Engineer

Tech Stack: Python, Librosa, CNN, MFCC, Streamlit, GTZAN Dataset, PyCharm IDE Duration: Independent Academic Project

Project Overview:

• I led the design, development, and deployment of a deep learning-based Music Genre Classification system to predict the genre of an audio track using convolutional neural networks (CNNs) and MFCC (Mel-frequency cepstral coefficients) as feature inputs.

• Enhanced system with predictive modeling and feature engineering techniques for audio classification.

• Designed with a focus on scalability, model interpretability, and compliance for real-time deployment.

• Applied model evaluation metrics (precision, recall, F1-score, ROC-AUC) to validate accuracy.

• The system was deployed as a web application using Streamlit, enabling real-time audio uploads and genre predictions. Title: Data Pipeline Automation

Role: Data Engineer

Tech Stack: Airflow, Snowflake, Python, SQL, Power BI, REST APIs Duration: Independent Project

Project Overview:

• Designed and implemented an Airflow-based orchestration pipeline to automate data ingestion from multiple REST APIs.

• Developed robust ETL workflows to extract, transform, and load large-scale datasets into a Snowflake data warehouse.

• Applied data lineage and validation checks to ensure data quality, accuracy, and governance compliance.

• Built interactive Power BI dashboards to visualize pipeline health, key metrics, and real-time business insights. EDUCATION

Georgia State University Atlanta, GA

Master of Science, Major in Computer Science August 2021 – May 2023 Jawaharlal Nehru Technological University Andhra Pradesh, India Bachelor of Technology, Major in Computer Science June 2016 – September 2020

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