satya chippada

Designed and implemented data analysis workflows to extract, cleanse, and transform Epic EHR data, enabling actionable insights into MyChart patient portal adoption and engagement trends.
Analysed large-scale patient encounter and appointment datasets using SQL and Python, supporting executive-level operational and revenue cycle reporting.
Automated recurring analytics tasks using Python scripts, reducing manual data preparation effort and improving reporting turnaround time.
Created interactive dashboards using Epic SlicerDicer outputs + Pulse graph outputs, translating raw EHR data into clinician- and leadership-friendly insights.
Supported EHR optimisation initiatives by validating data consistency across Epic patient data entry modules and identifying process inefficiencies impacting patient experience.

Designed and implemented data analysis workflows, Analyzed large-scale patient encounter and appointment datasets, Automated recurring analytics tasks using Python scripts, Created interactive dashboards, Supported EHR optimization initiatives

Designed, developed, and maintained production-grade Python applications supporting energy trading analytics and operational dashboards.
Built scalable ETL pipelines using Python, PySpark and SQL to ingest, cleanse, validate, and transform large volumes of trade, pricing, and market data from the Bloomberg terminal.
Implemented data quality checks, exception handling, logging, and audit trails to ensure accuracy, traceability, and compliance of financial data.
Developed backend services and internal APIs in Python to expose analytics to downstream systems and BI tools.
Automated end-to-end reporting workflows using Python scripts and schedulers like Cron and Apache Airflow, reducing manual effort and improving report turnaround time by ~40%.
Led web scraping and data ingestion initiatives using Requests and BeautifulSoup, migrating legacy data sources into centralised SQL databases.
Optimised SQL queries, indexes, and data models to improve performance for high-frequency and ad-hoc trading reports.
Collaborated closely with traders, analysts, and product owners to translate business requirements into reliable technical solutions.
Followed Agile development practices, participating in sprint planning, code reviews, and retrospectives.

Designed, developed, and maintained production-grade Python applications, Built scalable ETL pipelines using Python, PySpark, and SQL, Developed backend services and internal APIs in Python, Automated end-to-end reporting workflows using Python scripts, Led web scraping and data ingestion initiatives, Optimized SQL queries, indexes, and data models, Collaborated closely with traders, analysts, and product owners

Queried and analysed large datasets using Google BigQuery, enabling insights into course adoption, user behaviour, and content effectiveness.
Loaded data from an internally implemented data labelling initiative into BigQuery for analysis and usage in ML training.
Wrote optimised SQL queries in BigQuery and integrated results into Python applications for downstream analytics and reporting.
Collaborated with ML engineers to create Google Colaboratory, a free, hosted Jupyter notebook environment that requires no setup and has TensorFlow pre-installed, demo environments showcasing NLU and NLP models, improving adoption across Google 1P engineering teams.
Used Bootstrap to create responsive web applications, worked on building UI features for Google Conceptual Captions, and Google Natural Questions UIs for managing GARCON(Generation, Abstractization, Reading Comprehension On Neural architectures) research competitions.
Followed Agile development practices, participating in sprint planning, code reviews, and iterative releases aligned with Google engineering standards.

Queried and analyzed large datasets using Google BigQuery, Loaded data from an internally implemented data labeling initiative, Wrote optimized SQL queries in BigQuery, Collaborated with ML engineers to create Google Colaboratory, Used Bootstrap to create responsive web applications, Followed Agile development practices

Developed Python-based automation tools for cloud capacity management internally for cost optimisation, and infrastructure monitoring across 5,000+ servers.
Built data processing and analytics pipelines using Python (pandas, NumPy) and ElasticSearch to aggregate and analyse large-scale server logs to detect EOL machines and decommission them.
Designed and consumed REST APIs to integrate infrastructure data with internal dashboards and compliance platforms.
Created monitoring and compliance dashboards using ElasticSearch, Kibana, and custom Python scripts for servers.
Implemented CI/CD pipelines using Jenkins and Git, accelerating the deployment of Python services and automation jobs.
Performed advanced data analysis and visualisation using matplotlib and seaborn to deliver executive-ready performance reports.
Partnered with infrastructure, security, and compliance teams to meet audit and regulatory standards.
Used the Pyramid framework for authentication and authorisation using ACLs (Access Control Lists).

Developed Python-based automation tools for cloud capacity management, Built data processing and analytics pipelines using Python, Designed and consumed REST APIs to integrate infrastructure data, Created monitoring and compliance dashboards, Implemented CI/CD pipelines using Jenkins and Git, Performed advanced data analysis and visualization, Partnered with infrastructure, security, and compliance teams