Tinku Kumar is an experienced Azure Data Engineer with a strong background in data engineering, application development, and system design. He has successfully designed and maintained ETL processes and data pipelines, leveraging Azure Cloud services and various technologies to implement real-time analytics solutions. Tinku is adept at collaborating with cross-functional teams to gather requirements and deliver effective solutions.
Designed and developed scalable ETL processes and data integration solutions.
Implemented machine learning algorithms for predictive maintenance in IoT contexts.
Managed data integration from multiple sources using Airbyte.
Successfully designed and developed ETL processes and data pipelines for real-time analytics using Apache Airflow and Apache Pinot.
Overview: This project focused on building robust ETL processes and data pipelines to support real-time analytics. Responsibilities: Designed and developed ETL processes to extract, transform, and load data from various sources into a centralized data warehouse. Implemented data pipelines using Apache Airflow to automate data workflows and ensure timely data delivery. Utilized Apache Pinot for real-time data analytics, providing insights into data trends and patterns. Deployed solutions using Azure Blob Storage, Azure SQL DB, and AKS. Ensured data quality, integrity, and security throughout the ETL processes.
Key outcomes:
Successfully designed and developed ETL processes for real-time analytics.
Ensured timely data delivery through automated Apache Airflow pipelines.
Overview: This project focused on developing a predictive maintenance solution using real-time IoT data and machine learning. Responsibilities: Developed ETL pipelines to gather real-time data from IoT devices and sensors. Implemented machine learning algorithms to perform predictive maintenance and anomaly detection. Utilized Azure IoT Analytics and Azure Machine Learning for data processing and model training. Created visualizations and dashboards to monitor the health and performance of industrial equipment. Integrated IoT protocols such as OPCUA and MQTT for data collection.
Key outcomes:
Implemented machine learning algorithms for predictive maintenance and anomaly detection.
Created visualizations for monitoring industrial equipment health and performance.
Overview: This project developed custom web crawlers to scrape data from competitor websites for market analysis. Responsibilities: Developed custom web crawlers to scrape data from competitor websites and aggregate the data for market analysis. Implemented ETL pipelines to clean, transform, and load the scraped data into a data warehouse. Utilized Azure services for scalable data storage and processing. Developed data analytics using Apache Pinot.
Key outcomes:
Developed custom web crawlers for competitor market analysis.
Implemented ETL pipelines for cleaning and transforming scraped data.
Key outcomes:
Developed ETL processes for extracting and transforming data from scanned documents.
Ensured data accuracy and consistency through validation and error handling.
Key outcomes:
Developed custom web crawlers for competitor market analysis.
Implemented ETL pipelines for cleaning and transforming scraped data.
Tinku Kumar
Azure Data Engineer