Data Analyst Power Bi

MARMIK TANDEL

Texas (Open to Relocate), USA 469-***-**** ****************@*****.*** Portfolio SUMMARY

Detail-oriented Data Analyst with 4+ years of experience in extracting, transforming, and analyzing complex datasets to support data-driven decision-making in healthcare and financial services. Skilled in SQL, Python, R, and cloud platforms like AWS and GCP, with expertise in building ETL pipelines, developing predictive models, and creating interactive dashboards using Power BI and Tableau. Strong background in data warehousing, statistical analysis, and cross-functional collaboration to improve business intelligence and operational efficiency. SKILLS

Methodologies: SDLC, Agile, Waterfall, Kanban, Lean Six Sigma Languages: Python, SQL, R

IDEs: Visual Studio Code, PyCharm, Jupyter Notebook Packages: NumPy, Pandas, Matplotlib, SciPy, ggplot2, TensorFlow, Seaborn, Scikit-learn Visualization Tools: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP) Cloud Platform: Amazon Web Services (AWS), Google Cloud Platfrom (GCP) Database: MySQL, SQL Server, PostgreSQL, Oracle

Other Skills: Ad hoc Report, EDA, ETL Tools, Informatica Power Center, Machine Learning Algorithms, Deep Learning, NLP, Big Data Technologies, Spark, Probability distributions, Predictive Modelling, Hypothesis Testing, Regression Analysis, Linear Algebra, Advance Analytics, SAS, SSIS, SSRS, SSMS, Data Mining, Data warehousing, Data transformation, Clustering, Classification, Regression, A/B Testing, Forecasting & Modelling, Data Cleaning, Data Wrangling, Jira, Confluence, GitHub, Bitbucket

Operating System: Windows, Linux, Mac OS

EXPERIENCE

Data Analyst E&Y (Ernst & Young), TX Jun 2024 – Present

Extracted and prepared over 20 million financial transactions using SQL in BigQuery, consolidating customer activity, device metadata, and merchant profiles to create a unified dataset for fraud pattern analysis.

Designed and automated ETL workflows on Google Cloud Platform using Cloud Composer (Apache Airflow) and Dataflow, supporting daily ingestion and transformation of real-time fraud signals, and reducing manual intervention by over 90%.

Created custom fraud indicators such as location variance, transaction velocity, device ID frequency, and account behavior metrics using Python (pandas, NumPy) to enhance model features and improve signal-to-noise ratio in training data.

Trained and optimized classification models using scikit-learn (Random Forest, Logistic Regression) to score transactions by fraud risk; achieved an AUC of 0.91 and improved fraud detection precision by 38% over the legacy rules-based system.

Developed interactive dashboards in Power BI to visualize anomaly scores, regional fraud trends, and high-risk accounts; streamlined risk team workflows by enabling immediate access to daily alerts and priority scoring.

Integrated model outputs into the client’s fraud case management system and collaborated with compliance and audit teams to define actionable thresholds; insights contributed to a $1.2M reduction in fraud losses over two quarters and improved investigative efficiency. Data Analyst Softage Group, India Jul 2020 – Jul 2023

Engineered a centralized data integration framework for over 3.2 million patient records from multiple hospital systems (Cern er, Meditech, in-house HIS) using SQL Server Integration Services (SSIS) and Python (pandas, pyodbc), improving data accessibility across departments.

Automated data ingestion and transformation pipelines with SSIS and Python, handling daily batch processing of clinical datasets, including demographics, lab results, diagnoses, and vitals, reducing ETL runtime by 40% and maintaining a 99.9% data load success rate.

Developed over 30 custom validation rules in SQL and Python to identify and resolve data quality issues such as duplicates, missing values, and format inconsistencies. This improved the accuracy of patient records and reduced manual cleanup efforts by 50%.

Standardized incoming data into HL7 and FHIR formats in collaboration with IT and clinical informatics teams, enabling seamless integration across platforms and ensuring compliance with HIPAA and internal governance standards.

Performed statistical and visual analysis using Python (pandas, seaborn, matplotlib) to evaluate patient outcomes and treatment efficiency, supporting the implementation of targeted care plans that led to a 17% decrease in 30-day readmissions.

Built executive-level dashboards in Power BI to monitor trends in infection rates, length of stay, and readmission by department and condition. These dashboards enabled real-time operational insights across 12 hospitals.

Automated weekly reporting workflows with Python and Excel VBA, reducing turnaround time by 70% and eliminating manual errors in recurring performance summaries sent to department heads and leadership.

Documented data architecture, lineage, and transformation logic using Confluence and SharePoint, facilitating easier onboarding for new team members and ensuring audit-readiness for compliance reviews. EDUCATION

Master of Science in Business Analytics – East Texas A&M University, Commerce, Texas, USA Bachelor of Business Administration in Information Technology - Parul University, India CERTIFICATIONS

Google Data Analytics Professional Certificate

CS50: CS50's Introduction to Computer Science, Harvard University

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