Payal · Senior Azure Data Engineer · 5+ yrs
Mid-LevelAbout Payal
Payal is a Data Engineer with 5+ years of experience specializing in data pipeline automation and real-time data processing. She has a strong command of cloud-native solutions and has successfully deployed machine learning models in production environments.
5+ years of commercial experience in
Skills(9)
Why hire Payal?
Developed efficient data pipelines for unstructured data processing.
Implemented real-time data streaming architectures with Apache Kafka.
Designed and maintained robust ETL/ADF pipelines for large datasets.
Optimized database queries and managed large-scale data operations.
Enhanced operational efficiency by 30% through robust data solutions.
Reduced data processing time by 50% using Apache Kafka and Spark.
Achieved 99% data accuracy in fraud detection systems.
Project highlights(3)
Healthcare Data Management and Analytics – Data Engineer
Overview: Developed robust data engineering solutions for extensive healthcare data, including patient records, claims, and real-time analytics. Responsibilities: Managed large-scale healthcare datasets, utilized AWS services for data storage, and implemented ETL pipelines with Apache Airflow.
Key outcomes:
Enhanced operational efficiency and improved patient outcomes.
Supported data-driven decision-making through robust data solutions.
Fraud Detection System – Data Engineer
Overview: Developed an advanced fraud detection system utilizing machine learning models to identify and prevent fraudulent transactions. Responsibilities: Stored and managed structured data in Azure, employed machine learning techniques for model development, and implemented real-time data streaming using Apache Kafka.
Key outcomes:
Enhanced security and minimized losses by identifying fraudulent activities.
Enabled timely fraud detection alerts through real-time data analysis.
Customer Insights & Recommendations – Data Engineer
Overview: Developed a generative AI-powered application for predicting customer preferences and optimizing inventory management in the automotive industry. Responsibilities: Designed data architecture for AWS services, created ETL pipelines, and leveraged Generative AI models for personalized insights.
Key outcomes:
Enhanced customer satisfaction and drove sales through personalized recommendations.
Optimized inventory management by predicting customer preferences.
Industry experience
HealthTech
1 project- •Healthcare Data Management and Analytics— Data EngineerAWS · Spark · Python · Apache Airflow +1
AI / ML Platform
3 projects- •Healthcare Data Management and Analytics— Data EngineerAWS · Spark · Python · Apache Airflow +1
- •Fraud Detection System— Data EngineerAzure · Python · SQL · Machine Learning +1
- •Customer Insights & Recommendations— Data EngineerAWS · Machine Learning · Python · SQL +1
Automotive
3 projects- •Healthcare Data Management and Analytics— Data EngineerAWS · Spark · Python · Apache Airflow +1
- •Fraud Detection System— Data EngineerAzure · Python · SQL · Machine Learning +1
- •Customer Insights & Recommendations— Data EngineerAWS · Machine Learning · Python · SQL +1
Banking
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Typically responds within 4 business hours.
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Payal
Data Engineer