Data Analyst Power Bi

Niharika Suddala Data Analyst

Phone: +1-913-***-****) Email : *************@*****.*** LinkedIn

PROFESSIONAL SUMMARY

Highly skilled Data Analytics Engineer with over 3+ years of experience in analyzing large datasets, developing data models, and providing actionable insights to support business decisions. Expertise in SQL, Python (Pandas, NumPy), and R for data manipulation, statistical analysis, and building predictive models. Experienced in creating dynamic dashboards using Tableau and Power BI, as well as leveraging AWS services such as Redshift, Glue, and Lambda for data warehousing, ETL processes, and automation. Proficient in Excel, including advanced features like Power Pivots, VLOOKUP, and VBA macros, to streamline data transformation and reporting processes. Strong track record of collaborating with cross-functional teams to develop customized reporting solutions and drive business improvements. Adept at ensuring data integrity and compliance, particularly in regulated industries such as healthcare.

Passionate about turning complex data into clear, actionable insights that optimize operations and support data-driven decision- making. TECHNICAL SKILLS

Data Analysis & Programming: SQL, Python (Pandas, NumPy), R Data Visualization: Tableau, Power BI, AWS Quick Sight Data Warehousing & Modeling: Amazon Redshift, Data Modeling (Fact and Dimension Tables), Data Warehousing ETL & Data Pipeline: AWS Glue, AWS Lambda, DBT, Excel Macros Advanced Excel: Power Pivots, VLOOKUP, Array Functions, VBA Macros Cloud Platforms: AWS (S3, IAM, CloudWatch, Glue, Lambda, QuickSight) Statistical Analysis & Machine Learning: Python (Scikit-learn, Pandas), R, TensorFlow, Keras, Convolutional Neural Networks (CNN) Version Control & Collaboration: Git

Data Security & Compliance: HIPAA Compliance, AWS IAM Documentation: SQL Query Documentation, Data Workflow Documentation, Reporting Process Documentation WORK EXPERIENCE

Data Analytics Engineer Johnson & Johnson, LA Aug 2024 – Present

• Analyzed high-volume clinical trial and commercial datasets using SQL and Python (Pandas, NumPy) to generate insights supporting product development, patient safety, and sales optimization.

• Built end-to-end data pipelines using AWS Glue, Lambda, and S3 to automate ingestion and transformation of regulated healthcare data (GxP compliant).

• Designed and maintained data models in Amazon Redshift to support scalable analytics for R&D and supply chain divisions across J&J's global business units.

• Created interactive dashboards and visual reports in Tableau and Power BI for internal stakeholders and regulatory teams, tracking KPIs like clinical event rates, manufacturing efficiency, and global market performance.

• Supported pharmacovigilance and regulatory reporting teams by delivering validated data sets for safety monitoring and compliance with FDA and EMA standards.

• Developed and operationalized forecasting models using R and Python to anticipate demand across product lines, including surgical and pharmaceutical portfolios.

• Partnered with medical affairs, supply chain, and commercial teams translate business questions into actionable data queries and predictive analyses.

• Ensured compliance with HIPAA, GDPR, and internal data governance policies by working closely with security teams on access control, audit trails, and role-based data access using AWS IAM.

• Conducted data quality audits and implemented validation checks that improved downstream reporting accuracy by 30%.

• Streamlined Excel-based reports using Power Query, advanced formulas, and VBA macros, reducing manual reporting efforts across regulatory and QA teams.

• Led knowledge transfer and data literacy workshops for cross-functional stakeholders, ensuring effective use of dashboards and reports in decision- making processes.

Data Analytics Engineer Western Alliance Bank, AZ March 2024 – July 2024

• Designed and maintained robust ETL pipelines using SQL and Python to process high volumes of financial data including customer transactions, risk metrics, and loan performance data.

• Partnered with risk management and credit departments to develop data models that assessed loan default probability, enabling proactive risk mitigation strategies.

• Built interactive dashboards in Power BI and Tableau to visualize key financial indicators such as net interest margin, delinquency rates, and customer churn.

• Implemented data quality checks and automated anomaly detection to ensure accuracy and compliance across reporting systems.

• Integrated data from various internal sources (CRM, core banking system, loan servicing platforms) into centralized cloud data warehouses (e.g., AWS Redshift and Snowflake).

• Supported regulatory reporting (CCAR, DFAST) by validating data lineage, preparing audit trails, and ensuring compliance with FDIC and OCC guidelines.

• Collaborated with business analysts and compliance officers to automate recurring reports for departments including Treasury, Finance, and Internal Audit.

• Conducted time-series forecasting and cluster analysis to identify transaction trends, high-risk customers, and cross-selling opportunities.

• Participated in financial crime investigations by supplying relevant transactional data and insights to fraud detection teams using Python and SQL scripts.

• Applied advanced Excel (macros, Power Pivot, array formulas) to support finance teams in rapid budget analysis and variance tracking.

• Established data governance protocols and metadata documentation to maintain transparency and reproducibility in data analytics projects.

• Worked closely with IT security teams to enforce user-level data access control via AWS IAM and ensure secure financial data transmission. Business Data Analyst A.P. Moller- Maersk, Hyderabad, India Aug 2021 – June 2023

• Performed in-depth analysis of global shipping and supply chain data to identify patterns, delays, and cost inefficiencies, supporting strategic initiatives to improve logistics performance.

• Built interactive dashboards and reporting solutions in Power BI and Tableau to visualize key metrics like container turnaround time, route efficiency, and freight cost trends.

• Developed and optimized SQL queries to extract and transform large datasets from internal data warehouses, ensuring timely and accurate reporting to leadership and stakeholders.

• Collaborated with finance teams to analyze budget forecasts vs. actual logistics spend, highlighting areas for cost control and variance reduction.

• Automated recurring reporting workflows using Python and Alteryx, reducing manual efforts and improving data delivery timelines by 40%.

• Provided ad-hoc data support and actionable insights to operations managers for real-time decision-making around shipment delays, demurrage, and inventory flow.

• Participated in global cross-functional projects involving demand forecasting, customer segmentation, and KPI standardization across Maersk’s Ocean and inland services.

• Ensured data governance and quality assurance by establishing validation checks and resolving inconsistencies in shipment tracking and invoicing datasets

Education

Masters in Information Technology Project Management Indiana Wesleyan University Academic Projects

Forecast Pro: Predictive Analytics for Product Demand

• Developed forecasting models using Python (Scikit-learn, stats models) and R to predict product demand based on historical sales, seasonality, and market trends.

• Processed data using Pandas and NumPy, then validated model performance using RMSE and MAE metrics.

• Deployed insights via Power BI dashboards for stakeholders to optimize inventory and reduce overstock by 20%. Loan Lens: Credit Risk Evaluation System

• Engineered a scoring engine to assess loan default probability using SQL, Python, and machine learning models like logistic regression and random forests.

• Ingested and cleaned raw financial data with ETL pipelines, then segmented customer risk profiles for proactive intervention.

• Presented results in Power BI, enhancing transparency in risk-based loan decisions. Care Track: Medication Adherence Analysis Dashboard

• Conducted a retrospective study on over 50,000 patient records to analyze medication adherence using metrics like MPR and PDC.

• Queried and processed structured healthcare datasets using SQL and visualized patterns using Tableau and R ggplot2.

• Delivered insights on adherence gaps by demographic groups, helping shape patient engagement strategies.

Contact this candidate