Data Analyst Power Bi

Raghavendra Aasrith Dara

Data Analyst +* (***) **7- 4144

LinkedIn ***************.****@*****.***

Summary:

Data Analyst with around 4 years of experience transforming raw data into actionable insights that drive business performance and strategic decision-making. Skilled in SQL, Python (Pandas, NumPy, Matplotlib, Seaborn), Power BI, Tableau, and Excel for data cleaning, visualization, and reporting. Experienced in ETL workflows, data modeling, and automation using AWS, Azure, and Databricks to support scalable, cloud-based analytics solutions. Adept at building interactive dashboards and self-service BI solutions that empower stakeholders to track KPIs, optimize operations, and uncover trends. Strong background in statistical analysis, predictive modeling, and A/B testing to support data-driven initiatives. Proficient in integrating data from relational and NoSQL databases (SQL Server, MySQL, MongoDB, Snowflake) and applying advanced transformations. Collaborates closely with cross-functional teams in Agile environments to ensure the timely delivery of analytical solutions. Recently completed. Passionate about solving complex business problems through data, continuous learning, and applying modern analytics techniques.

Skills:

• Methodologies: SDLC, Agile, Waterfall, A/B Testing, hypothesis testing

• Languages: Python, R, SAS, SQL, PL/SQL

• IDEs: Visual Studio Code, PyCharm, Jupyter Notebook

• ETL Tools: SSIS, SSRS, SSAS, Informatica.

• Visualization Tools: Tableau, Power BI, Excel (V-lookup, H-lookup, Pivot Tables)

• Cloud Technologies: AWS, GCP, Azure

• Databases: MySQL, PostgreSQL, SQL Server, Oracle, MongoDB

• Version Control Tools: Git, GitHub, GitLab

• Other Skills: Data Cleaning, Data Wrangling, Critical Thinking, Communication, Presentation Skills, Problem-Solving, Data Analytics, Decision-making, Data Architecture, Data Mining, Data Management, Data Manipulation, Data Warehousing

• Operating Systems: Windows, Linux, Mac

Education:

• Masters in Science (Business Analytics and project Management), University of Connecticut, Hartford, CT, USA Certification:

• Azure associate level

Professional Experience

Johnson & Johnson - USA Data Analyst May 2024 - Present

• Faced fragmented reporting processes with inconsistent KPIs; developed Power BI dashboards integrating SQL and AWS datasets, enabling leadership to track patient outcomes in real time.

• Required centralized access to claims and clinical data; built ETL pipelines in AWS (S3, Redshift, Athena), reducing manual reporting by 40%.

• Encountered gaps in operational efficiency; conducted EDA with Python (Pandas, NumPy, Matplotlib), identifying bottlenecks and supporting root cause analysis that cut processing delays by 25%.

• Business stakeholders lacked visibility; collaborated to define KPI requirements and delivered self-service dashboards, improving decision-making speed by 30%.

• Needed to evaluate patient care initiatives; applied statistical modeling and hypothesis testing, boosting treatment accuracy predictions by 20%.

• Legacy Excel processes slowed reporting; migrated workflows into Power BI and Tableau, increasing scalability and saving 15 hours weekly.

• Salesforce and EMR data were inconsistent; built SQL Server integrations, ensuring clean and unified datasets for analytics.

• Compliance and audit demands increased; designed traceable pipelines with governance controls, ensuring HIPAA adherence and reducing audit preparation time by 50%. NextGen Healthcare Jan 2021 - Jul 2023

Data Analyst, India

• Executives lacked insight into loan risk; designed Tableau dashboards analyzing loan performance and customer behavior, improving portfolio risk management by 18%.

• Faced manual, error-prone ingestion; automated ETL with Azure Data Factory and Databricks, streamlining data pipelines and cutting load times by 40%.

• High churn rates impacted revenue; built predictive models in Python (Scikit-learn), improving churn prediction accuracy by 15% and supporting retention efforts.

• Data quality issues delayed analytics; implemented SQL profiling and integrity checks, reducing reporting errors by 25%.

• Needed real-time monitoring; created Power BI dashboards that gave leadership instant visibility into sales and marketing KPIs.

• Agile team required fast delivery; collaborated in sprints to deliver new KPIs and dashboards, improving time-to-market for insights.

• Marketing ROI was unclear; conducted A/B testing on campaigns, identifying successful strategies and boosting ROI by 12%.

• Long-running SQL queries slowed reporting; optimized SQL Server queries, reducing execution time by 35% and increasing dashboard responsiveness.

Projects

1. Sales Performance Analytics University of Connecticut

• Built an end-to-end data pipeline in Snowflake and Azure Data Factory, ingesting sales data from multiple sources.

• Designed Power BI dashboards to track revenue growth, customer segmentation, and product profitability trends.

• Applied SQL transformations and DAX calculations, improving executive decision-making with 95% data accuracy. 2. Healthcare Predictive Analytics Independent Project

• Developed a machine learning model using Python (Scikit-learn, Pandas, Matplotlib) to predict patient readmission risks.

• Performed EDA and feature engineering on clinical data, achieving 82% model accuracy in risk prediction.

• Built interactive Tableau dashboards to visualize patient outcomes and cost impacts for hospital management. 3. Customer Churn Dashboard Academic Project

• Integrated structured/unstructured customer data into Databricks for transformation and analysis.

• Created a Power BI dashboard with drill-down capabilities to monitor churn risk and customer lifetime value.

• Applied logistic regression and A/B testing to recommend retention strategies, reducing projected churn by 12%.

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