Data Analyst Power Bi

Prabhu Kurra

DATA ANALYST

Texas, USA +1-551-***-**** *************@*****.***

SUMMARY

Data Analyst with 4+ years of experience driving data-driven decision-making in healthcare and sales operations. Proficient in Python, SQL, and Power BI with strong expertise in statistical modeling, ETL processes, and cloud data platforms like AWS and Snowflake. Adept at creating predictive models and dashboards that optimize performance, reduce costs, and enhance stakeholder reporting.

Proven experience in sales and healthcare analytics, with transferable skills ideal for pharmaceutical performance and CRM analytics

SKILLS

Methodologies:

SDLC, Agile, Waterfall

Programming Language:

Python, SQL, R

Packages:

NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, dplyr, ggplot2

Visualization Tools:

Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP)

IDEs:

Visual Studio Code, PyCharm, Jupyter Notebook, IntelliJ

Database:

MySQL, PostgreSQL, MongoDB, SQL Server

Cloud Platform:

Amazon Web Services (AWS), Google Cloud Platform (GCP), Snowflake

Other Technical Skills:

SSIS, SSRS, SAS, Machine Learning Algorithms, ETL\ELT Tools, Statistics, ServiceNow, MapReduce, Alteryx, Google Big Query, Power Query, Probability distributions, Mathematics, Confidence Intervals, ANOVA, OLAP & OLTP and MS Office Suite (Microsoft Word, PowerPoint, MS Visio), Hypothesis Testing, Regression Analysis, Linear Algebra, Advance Analytics, Data Mining, Big Data, Data Integration, Data Interpretation, Data Pipeline, Data Visualization, Data warehousing, Data transformation, Data Governance, Data Storytelling, Association rules, Clustering, Classification, Regression, A/B Testing, Forecasting & Modelling, Data Cleaning, Data Wrangling, Descriptive analytics, Git, GitHub, JIRA, (VOC) Analytics, Qualtrics

Soft Skills:

Time Management, Leadership, Strategy Planning, Problem-Solving, Negotiation, Decision-Making, Documentation and Presentation, Analytical Thinking, Attention to Detail, verbal, and written communication

Operating Systems:

Windows, Linux, Mac OS

EXPERIENCE

Baylor Scott & White Health, TX Aug 2023 – Present

Data Analyst

Analyzed over 2.5 million Medicare claims records using SQL for efficient data querying and Python (Pandas, NumPy) for advanced data manipulation, identifying utilization patterns that supported cost containment and resource optimization.

Designed automated data workflows in Alteryx to streamline the preparation and transformation of healthcare claims data, reducing processing time by 30% and supporting real-time dashboard updates in Power BI.

Cleaned and validated EHR/EMR datasets using Python (Pandas), reducing data inconsistencies by 30%, ensuring high data integrity for predictive analytics and regulatory reporting.

Designed and optimized ETL workflows using SQL Server Integration Services (SSIS) to automate data ingestion and transformation from multiple sources, ensuring timely and accurate data delivery to stakeholders.

Created data visualizations using Seaborn and Matplotlib to track cost and utilization trends, delivering actionable insights that improved resource allocation by 20%.

Assessed and classified structured and unstructured healthcare data using internal data profiling tools; collaborated with governance teams to define sensitivity labels and implement privacy-focused access controls.

Collaborated with cross-functional campaign development teams to curate member-level healthcare communication lists using Python and SQL; performed QA on contact data and validated eligibility for outreach, enabling data-driven patient engagement.

Built predictive models using Random Forest and Linear Regression in Python (Scikit-learn) to forecast healthcare costs and predict service usage, reducing operational expenses by 15%.

Designed and deployed interactive Power BI dashboards for real-time claims data visibility, automating reporting processes and enhancing decision-making speed.

Utilized ANOVA and Logistic Regression in Python (SciPy) to identify cost drivers, leading to a 10% reduction in healthcare expenditures through strategic policy adjustments.

Utilized R for performing statistical analysis and exploratory data modeling on Medicare claims and patient utilization datasets, generating trend insights and predictive metrics that supported clinical cost-reduction initiatives.

Extracted and analyzed operational and financial data from SAP modules to support reporting on healthcare resource utilization and budget forecasting, integrating SAP outputs with Power BI dashboards and SQL-based ETL processes.

Streamlined ETL workflows using SQL Server Integration Services (SSIS) and Snowflake, optimizing data pipelines and enabling real-time analytics.

Partnered with cross-functional teams to integrate claims and survey data sources, enabling customer experience insights for Medicare service enhancements.

Ensured HIPAA compliance by managing sensitive Medicare data with encryption protocols and stringent data access controls, maintaining high data security standards.

Conducted root cause analysis on claims discrepancies using SQL and Python, minimizing claim rejections and enhancing billing accuracy.

Utilized MS Purview to scan and classify enterprise datasets within Azure Data Lake, supporting the definition of sensitivity labels and policy-based data access enforcement.

Leveraged Google Big Query for querying and analyzing large-scale datasets from EMR systems, integrating results into Python workflows for cost prediction modeling and regulatory insights.

Consolidated Medicare claims data into a unified data warehouse using AWS Glue and Snowflake, improving data accessibility and query performance for analytics.

Collaborated with actuarial analysts, policy experts, and healthcare administrators to align data analytics strategies with business objectives, driving targeted policy interventions.

Applied MapReduce concepts using Python for parallel processing of batch Medicare data across AWS cloud storage, accelerating aggregation and feature generation for downstream analytics.

Developed predictive models in Python (Scikit-learn) to identify at-risk beneficiaries, enabling proactive care management and contributing to a 12% improvement in member retention.

Worked closely with business users and IT teams to design and implement interactive reports and ad hoc queries using SSRS, enhancing decision-making through customized insights.

Capgemini, India Jan 2020 – Dec 2022

Data Analyst

Designed and implemented a centralized data warehouse using PostgreSQL to consolidate sales data from over 15 regions, increasing reporting efficiency by 30% and supporting strategic decision-making.

Developed automated ETL pipelines using Power Query to extract, transform, and load sales data from multiple sources (CRM, ERP, Excel), reducing manual processing errors by 25% and accelerating data processing by 40%.

Utilized SQL and PostgreSQL for complex queries, ensuring high-quality data for analytics and reporting, reducing report generation time by 30%.

Participated in the discovery and classification of CRM and ERP datasets across Oracle, Teradata, and SAS during the migration to Azure Synapse; ensured consistency with internal data management standards.

Used ServiceNow to manage incident tracking and change requests during sales data pipeline development, ensuring timely resolution and documentation of ETL-related issues.

Developed and maintained SSRS reports and dashboards for cross-departmental reporting needs, improving data visibility and reducing manual reporting workload by 40%.

Created recurring performance reports and dashboards using SAS and Excel to support stakeholders with strategic planning and initiative tracking.

Utilized Databricks unified analytics platform to develop scalable data pipelines using PySpark, enabling seamless integration of structured and unstructured healthcare data for analytics and reporting.

Developed and maintained SAS Enterprise Guide reports to validate new business initiatives, identifying defects and gaps before production rollout.

Designed pre-implementation audit reports using SAS and SQL by extracting data from Teradata, Oracle, and flat file systems to support change control and decision governance.

Automated monthly sales reports using Advanced Excel (Pivot Tables, VLOOKUP, Macros), streamlining workflows and improving accuracy and timeliness by 35%.

Conducted sales performance analysis using historical data to identify declining sales drivers, leading to a 10% sales improvement in underperforming regions.

Handled CRM and ERP data sources in sales reporting pipelines, ensuring data integrity and alignment with regional KPIs.

Supported internal audits by generating and validating classification reports using SAS and SQL, improving data privacy readiness across reporting pipelines.

Supported the development of data marts and reporting solutions for finance and business intelligence teams by maintaining data pipelines with SSIS and SQL Server.

Collaborated with Sales, Marketing, and Operations teams to define and standardize Key Performance Indicators (KPIs), aligning data analysis with business goals.

Implemented data governance protocols to ensure compliance with data management standards, enhancing data integrity and reducing discrepancies by 15%.

Continuously monitored sales data performance and enhanced reporting systems, conducting A/B testing to optimize sales strategies and improve data quality.

Analyzed sales patterns using SQL and Python, leveraging statistical methods to identify key drivers of performance and develop targeted sales strategies.

Utilized Python (Pandas, NumPy) for data wrangling, reducing data preparation time by 20% and accelerating insight delivery.

Performed ad-hoc analysis using SQL and Python, supporting strategic decision-making and contributing to a 12% increase in cross-selling opportunities.

Extracted and analyzed sales and operational data from SAP ERP modules to support dashboard development and KPI reporting in Power BI, enabling business users to track performance metrics in real time.

Automated manual quality assurance processes through parameterized SAS programs, reducing manual effort by over 40% and improving accuracy.

Analyzed customer interaction data from CRM platforms (Salesforce) to support targeted email marketing campaigns; generated actionable insights on engagement and conversion using SQL and Python.

Managed version control of SQL scripts and Python data models using Git and GitHub, enabling collaborative development and rollback support across BI and engineering teams.

Developed an interactive sales performance dashboard using Tableau, enabling real-time tracking of product and regional performance with detailed drill-down analysis.

Automated anomaly detection using SQL, enhancing early detection of performance issues by 30% and enabling proactive intervention.

Supported the migration of Oracle and SAS-based reporting systems to Azure Synapse and Data Lake, streamlining legacy workflows into scalable cloud-native solutions and improving query performance by 40%

Assisted in the migration of data workflows from on-premises SQL Server to Google Cloud Storage and Big Query, supporting cloud adoption initiatives and improving data pipeline scalability.

Conducted customer segmentation analysis using Clustering Algorithms in Python, driving targeted marketing campaigns and increasing sales conversion rates by 15%.

EDUCATION

Master of Science in Advanced Data Analytics – May 2024

University of North Texas, Texas, USA

Bachelor of Technology in Mechanical Engineering – May 2020

CMR College of Engineering and Technology, Telangana, India

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