Analyst

PRANEETH PAUL NIDADAVOLU

+1-913-***-**** *************@*****.***

PROFESSIONAL SUMMARY

Results-driven Data Analytics Engineer with 5+ years of experience transforming complex data into actionable insights to drive strategic decisions across legal, finance, outreach, and marketing functions. Proficient in SQL and Python (Pandas, NumPy) for writing efficient queries, performing deep-dive analysis, and building automated data workflows. Skilled in designing and maintaining ELT/ETL pipelines using dbt, AWS Glue, and AWS Lambda on cloud-based data warehouses including Amazon Redshift. Adept at building dashboards and self-service reporting solutions in Power BI, Tableau, and AWS QuickSight to visualize KPIs and business metrics clearly and effectively. Experienced in data modeling (star schema, fact/dimension tables), data governance, and HIPAA-compliant data handling. Strong collaborator skilled at translating stakeholder requirements into scalable data models and analytical solutions. Experienced with version control using Git/GitHub and Agile/Scrum methodologies. Committed to data storytelling, continuous improvement, and delivering high-impact insights in fast-paced environments. TECHNICAL SKILLS

Data Analysis & Reporting: SQL, Microsoft Excel (Power Query, Power Pivots, VLOOKUP, VBA Macros), Power BI, Ad Hoc Analysis, KPI Reporting

Programming & Scripting: Python (Pandas, NumPy, Scikit-learn), R, Bash/Shell Scripting Data Visualization: Tableau, Power BI, AWS QuickSight — dynamic dashboards, storytelling through visuals, executive reporting

Cloud & Database: Amazon Redshift, AWS S3, AWS Glue, AWS Lambda, AWS CloudWatch, AWS IAM; SQL- based querying, Data Modeling (Star Schema, Fact/Dimension Tables, Data Marts) ETL/ELT & Pipeline Automation: dbt (data build tool), AWS Glue, AWS Lambda, Excel Macros — building, extending, and optimizing data pipelines

Version Control & Collaboration: Git, GitHub — code reviews, pull requests, collaborative development, branching strategies

Statistical Modeling & ML: Scikit-learn, R, Python — predictive modeling, trend analysis, pattern detection, regression, classification

Methodologies: Agile/Scrum, Data Governance, HIPAA Compliance, Access Control (IAM), Data Documentation, Stakeholder Management

WORK EXPERIENCE

Data Analytics Engineer Sep 2024 – Present

Client: E2 Open

• Partnered with cross-functional stakeholders across departments to gather analytical requirements and translated them into structured data models, star schema designs, and scalable visual reporting solutions.

• Designed and maintained scalable ELT pipelines using AWS Glue, Lambda, and dbt to transform raw data into clean, well-modeled datasets supporting self-service analytics and business intelligence.

• Engineered subject-area data marts and star schema models in Amazon Redshift using dbt, improving data lineage, model maintainability, and analytical query performance.

• Built real-time operational dashboards in Power BI and AWS QuickSight to track healthcare KPIs, workflow efficiency, and cost performance metrics across business units.

• Conducted ad hoc analyses and deep-dive investigations using SQL and Python (Pandas, NumPy), delivering written insights and data visualizations that empowered executive and operational decision- making.

• Collaborated with engineering and product teams to define data requirements for new initiatives, ensuring seamless integration into existing pipelines and data models.

• Troubleshot, optimized, and documented dashboards and reporting queries, improving system reliability, accuracy, and response time for end users.

• Delivered insights to both technical and non-technical audiences through clear documentation, stakeholder presentations, and effective data storytelling.

• Enforced data security and compliance frameworks using AWS IAM and strictly adhered to HIPAA regulations when working with sensitive healthcare data.

• Actively participated in Agile/Scrum ceremonies including sprint planning, stand-ups, and retrospectives to support iterative delivery of new data models, dashboard features, and pipeline enhancements.

• Used Git and GitHub for version control and code collaboration, performing code reviews on SQL and dbt models to maintain consistent, scalable, and high-quality analytics practices. Environment: SQL, Amazon Redshift, AWS Glue, AWS Lambda, AWS S3, AWS CloudWatch, dbt, Python (Pandas, NumPy), R, Power BI, AWS QuickSight, Excel (Pivot Tables, VLOOKUP, VBA Macros), Tableau, HIPAA Compliance, AWS IAM, GitHub Business Data Analyst Intern Nov 2022 – Aug 2024

Client: Elevate

• Collaborated with internal stakeholders across finance, HR, and operations to gather data requirements and deliver SQL-driven insights into payroll trends, benefits utilization, and employee compensation metrics.

• Built and maintained interactive Tableau dashboards enabling business users to self-serve on operational KPIs, HR metrics, and compensation benchmarking.

• Defined and measured business metrics by translating stakeholder needs into clean, structured data models and report logic using SQL and Python (Pandas, NumPy).

• Automated repetitive manual reporting workflows using Excel VBA Macros and Power Pivots, significantly increasing reporting efficiency and reducing human error in recurring business reports.

• Contributed to data pipeline enhancements by preparing clean, transformation-ready datasets aligned with analytical goals and downstream reporting needs.

• Applied R for statistical analysis, pattern detection, and trend forecasting in employee datasets, providing insights that informed strategic benefits optimization decisions.

• Participated in Agile/Scrum ceremonies and daily stand-ups to align on sprint priorities, track analytics initiative progress, and support iterative data deliveries.

• Presented findings and insights through written summaries, data visualizations, and walkthroughs to help stakeholders understand trends and take data-driven action.

• Supported dashboard troubleshooting and root cause analysis by identifying data mismatches, pipeline bottlenecks, and performance lags.

• Used Git and GitHub for version control, documentation, code reviews, and collaborative enhancement of shared SQL scripts and reporting logic.

Environment: SQL, Python (Pandas, NumPy), R, Tableau, Excel (Power Pivots, VLOOKUP, VBA Macros), dbt, Data Marts, Data Lakes, Git, GitHub, Reporting Automation, Dashboards, Data Analysis, Agile/Scrum Data Analyst Aug 2021 – Jun 2022

• Developed a Convolutional Neural Network (CNN) model using TensorFlow and Keras for real-time face mask detection, achieving 92% accuracy across diverse lighting and camera conditions.

• Preprocessed and augmented image datasets using OpenCV to ensure model generalization across varying image orientations, resolutions, and lighting environments.

• Integrated the trained model with live camera inputs via OpenCV for real-time video stream inference, enabling accurate face mask detection in production-like conditions.

• Fine-tuned model hyperparameters to optimize detection accuracy, inference speed, and resource efficiency for real-time application deployment.

• Collaborated with senior engineers and the data science team to evaluate, improve, and document model performance using precision, recall, F1-score, and confusion matrix metrics. Environment: Python, TensorFlow, Keras, OpenCV, Git, Scikit-learn, CNN, Deep Learning EDUCATION

Master's in Information Technology and Management

Campbellsville University

Bachelor of Technology in Electrical and Electronics Engineering JNTUH

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