Machine Learning Data Analyst

Manohar Chedalu

Sr. Data Analyst

Email: ************@*****.*** Number: 626-***-****

Professional Summary

• Over 4+ years of experience in data engineering, machine learning, cloud-based solu<ons, and business intelligence across industries like healthcare, banking, and business solu<ons.

• Proficient in building cloud-na<ve applica<ons and scalable data pipelines on AWS, Google Cloud PlaForm, and Azure, ensuring high availability and performance.

• Exper<se in ETL design and automa<on using Apache Airflow, Talend, Informa<ca, AWS Glue, and Azure Data Factory, op<mizing data workflows and reducing processing <me.

• Strong hands-on experience with machine learning frameworks, including scikit-learn, TensorFlow, XGBoost, LightFM, and AWS Sagemaker, for predic<ve analy<cs and anomaly detec<on.

• Developed <me-series forecas<ng models using ARIMA, LSTM, and Facebook Prophet to predict demand, op<mize inventory, and support business planning.

• Extensive knowledge of DevOps prac<ces with exper<se in CI/CD pipelines using Jenkins, GitHub Ac<ons, and container orchestra<on with Docker and Kubernetes.

• Skilled in serverless architecture implementa<on using AWS Lambda, AWS Fargate, and Azure Func<ons, improving scalability and reducing infrastructure costs.

• Designed and maintained real-<me dashboards using Tableau, Power BI, and Grafana, providing ac<onable insights for key business metrics and clinical trial performance.

• Experience in managing cloud-based data storage and processing frameworks, including AWS RedshiX, Google BigQuery, MicrosoX SQL Server, and PostgreSQL.

• Applied advanced sta<s<cal models using Python, R, and SAS for survival analysis, regression modeling, and <me-to-event analysis to improve business outcomes.

• Integrated real-<me streaming data from IoT devices and external APIs into Google BigQuery and AWS RedshiX, enhancing data freshness and analy<cs capabili<es.

• Collaborated with cross-func<onal teams, including business stakeholders, data engineers, and DevOps specialists, to deliver data-driven solu<ons aligned with strategic goals.

• Implemented data governance and security measures using Collibra, AWS IAM, and OAuth 2.0, ensuring compliance with HIPAA, FDA, PCI-DSS, and SOX regula<ons.

• Enhanced applica<on performance and op<mized SQL databases for high availability and disaster recovery using Amazon RDS and Azure SQL Database.

• Developed microservices-based data pipelines for seamless integra<on with AWS S3, Google Cloud Storage, and other external systems.

• Applied advanced observability and monitoring prac<ces with Splunk, Prometheus, and Grafana, reducing incident resolu<on

<me by 30%.

• Led ini<a<ves to migrate legacy data systems to Google Cloud PlaForm and AWS, improving data accessibility, scalability.

• Mentored and trained junior data analysts and developers in cloud-based data engineering, machine learning best prac<ces, and DevOps tools, fostering a collabora<ve learning environment.

• Proven ability to troubleshoot and resolve complex data issues, perform root cause analysis, and ensure minimal down<me in produc<on systems

Technical Skills

Programming Languages Python, R, SQL, Java, C#, .NET Machine Learning & Sta<s<cal

Modeling

scikit-learn, TensorFlow, XGBoost, LightFM, LSTM, ARIMA, Facebook Prophet, Regression Analysis, Survival Analysis, Clustering, Hypothesis Tes<ng, Predic<ve Modeling, Anomaly Detec<on, Time-Series Forecas<ng, AWS Sagemaker, Google Vertex AI Data Integra<on & ETL Tools Apache Airflow, Talend, Informa<ca, AWS Glue, MicrosoX SQL Server, PostgreSQL, AWS RedshiX, Google BigQuery, Azure Data Factory, SQL Alchemy Data Governance Collibra, Informa<ca, AWS IAM

Cloud PlaForms AWS, Google Cloud PlaForm, Azure

Data Visualiza<on & Repor<ng Tableau, Power BI, Looker, Google Data Studio, Excel, Grafana, Splunk Version Control & Collabora<on Tools Git, GitHub, Jira, Confluence, SonarQube Business Intelligence & Data Analysis EDA using Python, R, SQL, KPI Tracking, Fraud Detec<on, Dynamic Pricing Models, Customer Segmenta<on, Business Opera<ons Op<miza<on API & System Integra<on RESTful APIs, GraphQL, API Development, Microservices Architecture, Docker, Kubernetes, AWS API Gateway, Real-Time Streaming, IoT Data Integra<on Agile Methodology & Development

Tools

Jira, Jenkins, Docker, Kubernetes, React, Angular, GitHub Ac<ons, Terraform Security & Compliance PCI-DSS, SOX, HIPAA, FDA, ICH-GCP, Secure Coding Prac<ces, Role-Based Access Control, Data Lineage

Other Tools & Technologies AWS Fargate, AWS Lambda, Prometheus, Splunk, Elas<csearch, AWS CloudWatch EducaFon

• Masters in Computer Science from University of Central Missouri Professional Experience

Sr. Data Analyst

HCA Healthcare, Las Vegas, Nevada

October 2023 – Present

• Led data integra<on and transforma<on projects leveraging AWS RedshiT, Google Cloud BigQuery, and Azure Data Factory to manage large-scale clinical trial data pipelines.

• Built scalable, cloud-na<ve ETL workflows using Apache Airflow and AWS Glue, automa<ng data inges<on from FHIR- compliant EHR systems, IoT devices, and third-party clinical systems.

• Developed predic<ve analy<cs models for pa<ent risk stra<fica<on and clinical outcome predic<on using Google Vertex AI and AWS Sagemaker.

• Designed real-<me dashboards and repor<ng solu<ons on Tableau and Power BI, integrated with AWS RedshiT and BigQuery, enabling real-<me clinical metrics tracking and opera<onal performance insights.

• Deployed and managed data storage and processing frameworks on AWS S3 and Google Cloud Storage, ensuring efficient and secure data management.

• Applied advanced sta<s<cal models using Python (NumPy, Pandas, SciPy), R, and SAS for survival analysis, <me-to- event modeling, and treatment efficacy assessments.

• Migrated legacy data systems to the Google Cloud Pla_orm (GCP) and AWS, improving data accessibility, scalability.

• Collaborated with cross-func<onal teams to define cloud data architecture strategies and implement serverless soluFons for scalable analy<cs.

• Developed containerized machine learning models using Docker and orchestrated deployments with Kubernetes on GCP and AWS Fargate.

• Enhanced data governance processes using Collibra and AWS IAM, ensuring compliance with HIPAA, FDA, and SOX regula<ons.

• Integrated BigQuery ML and AWS Athena for ad hoc query capabili<es, improving data explora<on and insight genera<on for clinical researchers.

• Mentored junior analysts on cloud-based data engineering best prac<ces and advanced machine learning model deployment in cloud environments.

Environment: Python, R, SAS, SQL, scikit-learn, TensorFlow, Keras, Tableau, Power BI, MicrosoX SQL Server, PostgreSQL, AWS RedshiX, Google Cloud PlaForm, BigQuery, AWS Sagemaker, Apache Airflow, AWS Glue, Docker, Kubernetes, Azure Data Factory, AWS IAM, FHIR, HL7, HIPAA, FDA, ICH-GCP

Senior BI Developer

US Bank, Topeka, kS

January 2023 –July 2023

• RESTful APIs and GraphQL endpoints to manage secure communica<on between services, ensuring robust data exchange across internal and external systems.

• Enhanced CI/CD pipelines with Jenkins, GitHub AcFons, and Terraform, automa<ng deployment, infrastructure provisioning, and version control for faster releases.

• Implemented observability pracFces with Splunk, Prometheus, and Grafana, enabling real-<me monitoring and proac<ve issue resolu<on in produc<on environments.

• Containerized applica<ons using Docker and orchestrated deployments with Kubernetes, ensuring consistency and scalability across development, staging, and produc<on environments.

• Collaborated closely with DevOps and SRE teams to design fault-tolerant systems, implement blue-green deployments, and ensure high availability of services.

• Spearheaded the moderniza<on of U.S. Bank’s digital banking plaForms by migra<ng legacy applica<ons to microservices architecture, deployed on AWS Fargate and Azure Kubernetes Service (AKS) for improved scalability and availability.

• Developed and maintained cloud-na<ve, scalable applica<ons using Java, C#, .NET, and SQL, op<mizing performance for high-transac<on-volume systems.

• Built serverless func<ons using AWS Lambda for real-<me data processing and integra<on with banking services, improving responsiveness and reducing infrastructure costs.

• Designed and integrated

• Applied serverless architecture with AWS Lambda for asynchronous task execu<on and event-driven workflows, improving efficiency and scalability.

• Worked on op<mizing SQL databases for high availability and disaster recovery using Amazon RDS and Azure SQL Database, ensuring data integrity and performance.

• Implemented advanced security measures, integra<ng OAuth 2.0, AWS IAM, and Azure AcFve Directory to manage user authen<ca<on and secure sensi<ve data access.

• Developed role-based access control (RBAC) policies in Kubernetes clusters to secure workloads and manage permissions for various development teams.

• Collaborated with cross-func<onal teams, including product managers, business analysts, and DevOps engineers, to priori<ze features, improve development workflows, and deliver business value.

• Mentored junior developers and DevOps engineers on cloud best pracFces, Kubernetes administraFon, and modern CI/CD pipelines, fostering a culture of innova<on and con<nuous improvement.

• Integrated serverless event-driven architecture for key business processes using Amazon SQS, SNS, and DynamoDB Streams to handle asynchronous communica<on.

Environment: Java, C#, .NET, SQL, AWS (Lambda, Fargate, RDS, SQS, SNS), Azure (AKS, Azure SQL Database, Active Directory), Docker, Kubernetes, Jenkins, GitHub Actions, Terraform, Grafana, Prometheus, Splunk, RESTful APIs, GraphQL Data Analyst

MU Sigma Business SoluFons Pvt Ltd

October 2020– December 2022

• Led large-scale data integra<on and transforma<on projects using Apache Airflow, Talend, and Azure Data Factory, enabling automated and scalable ETL pipelines for real-<me data processing.

• Built real-<me data analyFcs pla_orms on Google BigQuery and AWS RedshiT, ensuring high availability and quick response <mes for business-cri<cal opera<ons.

• Developed advanced Fme-series forecasFng models (ARIMA, LSTM, Facebook Prophet) for demand predic<on and op<mized inventory management across retail and consumer goods sectors.

• Designed and implemented fraud detecFon systems using Python (scikit-learn, XGBoost), Splunk, and ElasFcsearch, iden<fying suspicious ac<vi<es with a 95% detec<on accuracy.

• Created real-<me monitoring dashboards and aler<ng systems using Tableau, Power BI, and Splunk, providing ac<onable insights.

• Deployed machine learning models for customer segmenta<on, recommenda<on systems, and anomaly detec<on on AWS Sagemaker and Google AI Pla_orm, enhancing business intelligence.

• Conducted dynamic pricing analysis using Python to op<mize revenue by adjus<ng prices based on real-<me demand, compe<tor pricing, and customer behavior trends.

• Collaborated with cross-func<onal teams to build microservices-based data pipelines, ensuring seamless integra<on with cloud storage solu<ons such as AWS S3 and Google Cloud Storage.

• Automated repor<ng and data extrac<on processes using Python (pandas, SQLAlchemy) and SQL, significantly improving opera<onal efficiency and accuracy.

• Implemented data governance frameworks with Collibra, InformaFca, and AWS IAM, ensuring compliance with data security and regulatory standards.

• Integrated real-Fme streaming data from IoT devices and external APIs into Google BigQuery and AWS RedshiT, improving data freshness and analy<cs capabili<es.

• Enhanced cloud-based data warehouse architectures, enabling faster querying and cost op<miza<on for large-scale analy<cs.

• Worked with CI/CD pipelines using Jenkins and GitHub AcFons for deploying machine learning models and data services on AWS Lambda and Azure FuncFons.

• Mentored junior analysts in cloud-na<ve tools and machine learning best prac<ces, fostering a data-driven culture within the organiza<on.

Environment: SQL, Python (pandas, NumPy, scikit-learn, XGBoost), R, Apache Airflow, Talend, Informatica, Power BI, Tableau, Google Data Studio, AWS Sagemaker, Google BigQuery, AWS Redshift, Azure Data Factory, Splunk, Elasticsearch, Collibra, AWS IAM, Jenkins, GitHub Actions, AWS Lambda, Azure Functions

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