Payal · Senior Azure Data Engineer · 5+ yrs
Mid-LevelAbout Payal
Payal has over 5 years of experience in data engineering, focusing on building efficient data pipelines and managing complex workflows. She has a proven track record in implementing real-time data streaming architectures and automating ETL processes across AWS and Azure platforms. Her expertise includes distributed data processing and machine learning model deployment, ensuring data accuracy and system efficiency.
Core expertise
Additional skills(9)
AWSWhy 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.
5+ years of industry experience
- •Healthcare Data Management and Analytics— Data EngineerAWS · Spark · Python · Apache Airflow +1
- •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
- •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
At a Glance
Single contract. Billed in USD.
Typically responds within 4 business hours.
Top Skills
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Payal
Data Engineer