Healthcare-Focused Python Backend Developer with API & ETL Expertise

Rambabu Simhadri

Location 928-***-**** ************@*****.***

LinkedIn: https://www.linkedin.com/in/s-rambabu-91341b19b/ PROFESSIONAL SUMMARY

Python Developer with 3 years of hands-on experience delivering scalable, secure, and high-performance backend solutions in the IT and Healthcare domains. Proven track record of designing and deploying RESTful APIs using FastAPI and Flask, and building robust ETL pipelines with Apache Airflow, Pandas, and SQLAlchemy to automate the processing of large-scale healthcare data, including EHR and claims. Demonstrated expertise in implementing healthcare interoperability standards such as FHIR and HL7, enabling seamless real-time data exchange across provider networks. Adept at working with HIPAA-compliant systems, applying secure authentication methods (OAuth2, JWT) and encryption protocols (PyCrypto) to protect sensitive patient data. Strong command over data analysis and visualization using tools like NumPy, SciPy, Power BI, Tableau, and Seaborn to support clinical insights and operational reporting. Skilled in containerization and cloud infrastructure using Docker, Kubernetes, and AWS, with a focus on improving deployment efficiency and system scalability. Highly collaborative, with experience contributing to Agile teams at organizations like Blue Cross Blue Shield, Uber, and Wipro, consistently delivering reliable solutions that meet compliance, performance, and business goals.

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TECHNICAL SKILLS

Programming & Scripting Python, Shell Scripting

Data Analysis & Processing Pandas, NumPy, SciPy, Statsmodels Data Visualization Matplotlib, Seaborn, Plotly, Jupyter Notebooks Healthcare Standards HL7, FHIR, FHIR-Py, PyMedTermino, pydicom Databases & ORM PostgreSQL, MySQL, SQLite, MongoDB, SQLAlchemy, PyODBC Web & API Development FastAPI, Flask, Django, Swagger, OpenAPI, RESTful API design Asynchronous Processing Celery, Redis, RabbitMQ

Cloud Platforms AWS (Lambda, EC2, S3, RDS), Azure (App Services, Cosmos DB) DevOps & Deployment Docker, Kubernetes, Git, GitHub, GitLab, CI/CD pipelines Security & Compliance OAuth2, JWT, PyCrypto, cryptography, HIPAA-compliant design ETL & Workflow Orchestration Apache Airflow, Luigi Business Intelligence Power BI, Tableau, Apache Superset, Excel (Advanced) Testing & QA PyTest, unittest, Postman, Locust

Documentation & Collaboration Swagger, OpenAPI, Confluence, Jira, Markdown, Sphinx EDUCATION

Northern Arizona University Aug 2022- May 2024

Masters in Information Technology

CGPA: 3.36/4.0

K L University Jul 2018- Apr 2022

Bachelors in Electronics & Communication Engineering CGPA: 3.0/4.0

WORK EXPERIENCE

Python Developer May 2025 – Present

Blue Cross Blue Shield, USA

Designed and deployed secure RESTful APIs for member and claims data using FastAPI and PostgreSQL, improving system response time by 30% through optimized query design and indexing strategies.

Built and orchestrated scalable ETL pipelines using Apache Airflow, Pandas, and SQLAlchemy, resulting in 40% faster data processing from EHR systems and third-party healthcare data sources.

Enabled interoperability across provider networks by integrating FHIR and HL7 APIs, reducing manual reconciliation efforts by 50% and supporting real-time patient data exchange.

Analyzed over 5M+ rows of structured and unstructured healthcare data using NumPy, Pandas, and SciPy to support clinical decision-making and risk scoring models.

Developed dashboards using Power BI and Seaborn to visualize care quality, cost trends, and operational KPIs, improving stakeholder reporting efficiency by 35%.

Ensured HIPAA compliance by implementing secure authentication (OAuth2, JWT) and encryption protocols (PyCrypto), reducing audit remediation tasks by 70%.

Containerized applications using Docker and orchestrated deployments with Kubernetes on AWS, improving system scalability and reducing deployment time by 60%.

Collaborated cross-functionally with QA, DevOps, and data science teams in Agile sprints to deliver reliable, secure, and scalable healthcare IT solutions within critical deadlines. Python Developer Sep 2024 – Apr 2025

Rackspace, USA

Developed and deployed 10+ scalable RESTful APIs using FastAPI to support healthcare data integration, enabling real-time transport scheduling for 20+ hospitals.

Improved data pipeline reliability by 30% by building ETL workflows in Python and Airflow that ingested and transformed patient and provider data from multiple sources.

Reduced deployment time by 40% by containerizing services using Docker and automating deployment with AWS ECS and CloudWatch.

Created automated reports and dashboards using SQL and Tableau, enabling executives to monitor healthcare KPIs such as trip efficiency and patient wait times.

Achieved a 15% decrease in patient no-show rates by analyzing mobility data with Pandas and implementing a real-time trip tracking solution.

Partnered with cross-functional teams including DevOps and Compliance to ensure HIPAA-aligned backend infrastructure and high service availability.

Decreased pipeline downtime by 30% by designing monitoring and alerting systems to catch data anomalies and job failures proactively.

Python Developer Aug 2020 – Jul 2022

Wipro, India

Built scalable backend services and microservices using Python (Flask, FastAPI) to handle healthcare data processing, resulting in a 40% improvement in system performance by modernizing legacy systems into microservice-based architecture.

Automated data extraction and transformation pipelines using Pandas, PySpark, and Apache Airflow, increasing data processing efficiency by 50% and enabling near real-time reporting of Electronic Health Record (EHR) and claims data.

Developed and maintained RESTful APIs for secure exchange of healthcare data in compliance with HIPAA, improving integration speed with external providers by 30%.

Created validation and anomaly detection scripts that reduced healthcare billing errors by 25%, using statistical methods and Python libraries like NumPy and SciPy.

Led the migration of healthcare data from monolithic systems to PostgreSQL and MongoDB, improving data accessibility and reducing query time by 35%.

Enhanced automated reporting workflows using SQL and Python, reducing manual reporting efforts by 60% and improving report delivery timelines for healthcare operations.

Collaborated with cross-functional teams (DevOps, QA, BI analysts) in an Agile environment using JIRA and Confluence, ensuring on-time sprint deliveries with over 95% completion rate.

Implemented containerization using Docker and automated deployment with Jenkins, improving CI/CD reliability and reducing deployment errors by 20%.

Monitored application performance using Prometheus, Grafana, and ELK stack, proactively identifying and resolving performance issues to maintain 99.9% system uptime. PROJECTS

Human activity Recognition

Human activity recognition or HAR for short is a board field of study concerned with identifying the specific movement or action of a person based on sensor data

Movement are often typical activities performed indoors such as walking, talking standing and sitting Cardiac Disease Prediction By Analyzing ECG Signal

Our project proposes a method of detecting Arrhythmia using the raw ECG signal the multiple parameters of an ECG signal are used for assessing the status of heart disease. The objective is used to find a way to improve the detection of heart disease data with better Accuracy. our method performs well across various parameters, and it has an overall performance of 98.74%.

Machine Learning and Deep Learning for ECG data have become more prevalent in recent years. The project shows that a hybrid approach that combines recurrent neural networks and a convolutional network achieves the best

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Defect characterization in infrared non- destructive testing with machine learning:

The project aims to characterize defects automatically using the Principal Component Analysis method which is one of the machine learning algorithms.

In this project, we captured thermal images from the surface of the CFRP (Carbon fiber reinforced polymer) objects and using Matlab we performed some actions and then derived a sequence of data in the form of a dataset and then trained PCA and predicted the defects. Iris Flower Classification

In this project we collected a dataset consisting of sepal length, petal length, sepal width, petal width and by using K nearest neighbor algorithm (KNN) which is a classification algorithm in machine learning.

I have classified the flowers into different classes and predicted the class of which the given data belongs to.

Home Security System

It is an IOT based project, In this project we have monitored the temperature and humidity in a room and uploaded them into Thing speak cloud using MQ135 gas sensor and it will rings an alarm if the temperature exceeds the threshold limit.

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