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I will automate data processing using python, pandas, pyspark, and azure data factory


Ramanagouda P
About this gig
Are you dealing with slow data processing, messy datasets, or manual Python scripts that dont scale?
I help businesses clean, process, and automate data efficiently using Python, PySpark, and Pandas.
With 11+ years of hands-on Python experience, I specialize in building reliable, scalable, and well-structured data processing solutionsfrom small datasets to large distributed workloads.
What I Can Do for You
- Build and optimize PySpark data pipelines for large-scale data processing
- Develop Python & Pandas-based data transformations
- Clean, validate, and standardize messy datasets
- Convert business logic into efficient Python workflows
- Optimize slow Python/Pandas code for better performance
- Handle structured & semi-structured data (CSV, JSON, XML, Parquet)
- Add logging, error handling, and validations for production use
- Prepare clean, analytics-ready datasets
Why Clients Choose Me
11+ years of real-world Python experience
Strong expertise in PySpark & Pandas
Focus on clean, readable, maintainable code
Proven ability to reduce manual effort and processing time
Clear communication and reliable delivery
Perfect For
- Data teams needing Python-based data processing
- Businesses working with large
Get to know Ramanagouda P
Ramanagouda P
- FromIndia
- Member sinceApr 2021
Languages
Kannada, English, Hindi, Telugu
I am a Certified Azure Data Engineer Associate with 11+ years of specialized experience building high-performance, scalable data pipelines on the Azure and Databricks platforms.
My core expertise:
PySpark & Databricks: Optimizing large datasets and transforming legacy SQL into efficient PySpark solutions using AQE and partitioning.
Azure: ADF, ADLS, and CLI for robust ETL/ELT orchestration.
Python Automation: Developed advanced bots (OCR, JSON/XML parsing) and custom GUI tools, leading to up to 40% efficiency gains.
BI: Power BI dashboard creation with complex DAX for data-driven insights.
