
Pavan Kumar M
Technology / Internet
Services offered
Working as Data Engineer at Startup for IoT Product client and previously for Britannia FMCG.
I have skills in Python, Mysql, MongoDb, Redis, Airflow, AWS (S3, Cloudwatch, lambda and Redshift), Docker and Kafka. Currently working on my Pyspark things.
Experience
Britannia BrandOps Sales Analysis Data Pipeline
•
Engineered a comprehensive ETL pipeline for a leading FMCG brand, streamlining the extraction and transformation
of sales data, enhancing reporting accuracy and speeding up decision-making processes by 40 hours monthly.
•
Optimized SQL queries and data processing workflows, reducing execution time by 30% and improving system
efficiency.
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Integrated AWS services (S3, Lambda, Redshift) with data processing pipelines, enabling real-time analytics and
streamlining reporting processes.
•
Developed interactive dashboards using Tableau, enhancing data visualization and accessibility for stakeholders,
thereby supporting business intelligence efforts.
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Worked with cross-functional teams to implement data warehousing solutions, enhancing data accessibility and
supporting business intelligence initiatives.
•
Managed Dockerized applications for efficient deployment and container orchestration, ensuring seamless
integration with Apache Airflow for task automation.
Smart Grow Agritech IoT Product Data Pipeline
•
Led the Smart Grow project aimed at optimizing plant growth data management and quality control operations,
improving data processing speed by 50%.
•
Engineered robust data pipelines to process over 10,000 sensor data points and integrate them with lab results for
real-time tracking and analysis using Kafka, MongoDB, and Airflow.
•
Developed a system that automatically creates and updates mileposts, providing insights into 95% of plant growth
stages and quality metrics.
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Structured data to ensure seamless integration with QBench, improving workflow efficiency by 30%.
•
Leveraged Docker and Airflow for automating task orchestration and containerized deployments, facilitating
efficient handling of large data volumes.
•
Processed over 1 million records, significantly improving decision-making for agronomic operations
Education
bachelor's of Education (Electronic and Communications Engineering)
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