Cloud-Native Data Processing and EngineeringArchitectured and implemented robust cloud-native data processing pipelines leveraging AWS Lambda for serverless functions, optimizing costs and scalability.Orchestrated complex data workflows using Apache Airflow, ensuring efficient and reliable execution of ETL processes.Monitored and optimized data pipeline performance utilizing AWS CloudWatch, implementing proactive alerts and dashboards.Integrated Informatica PowerCenter for seamless data integration, designing and developing ETL pipelines in both Informatica and Python.Machine Learning and Predictive AnalyticsCollaborated with data scientists to deploy machine learning models in Python, driving predictive analytics for agricultural data.Utilized Apache Spark for large-scale data processing and real-time insights via Spark Streaming, handling terabytes of agricultural sensor data.Optimized MySQL databases for efficient data storage and retrieval, supporting analytics and reporting.Accelerated data processing and machine learning model training with Databricks on Apache Spark.Data Engineering and InfrastructureContainerized data applications using Docker for consistent deployment across environments.Implemented CI/CD pipelines with Drone and GitHub Actions to automate testing and deployment processes.Established comprehensive monitoring and logging solutions using Datadog for system health and performance tracking.Designed and optimized Snowflake data warehouse solutions for analytics and reporting, including schema design and data loading.Data Analysis and VisualizationDeveloped interactive dashboards and reports using Power BI and Tableau to communicate data insights to stakeholders.Conducted data analysis using Excel, creating pivot tables, charts, and automating tasks with macros and formulas.Utilized Postman for API testing and debugging to ensure data integration quality.

Optimized data ingestion and processingImplemented Apache NiFi to streamline data transfer from local file systems to HDP clusters, resulting in a 30% increase in efficiency.Developed robust ETL pipelines Leveraged Spark, Pyspark, and Spark-SQL to create scalable ETL solutions, handling 60 terabytes of data per day.Implemented real-time data processing:Utilized Spark Streaming to process 600 events per second from Kafka, enabling near-instantaneous data analysis and response.Migrated and optimized big data infrastructureSuccessfully migrated Cassandra and Hadoop clusters to AWS, improving performance by 30% through optimized read/write strategies.Enhanced data quality and validationDeveloped comprehensive data validation processes using SQL queries, ensuring data integrity and accuracy.Implemented innovative data solutionsDesigned and implemented custom data pipelines using Informatica and Spark to address specific business requirements, resulting in 30% time savings.Leveraged advanced data analyticsUtilized Flume, Pig, Hive, HBase, Oozie, Zookeeper, Sqoop, Spark, and Kafka to uncover valuable insights from large datasets, leading to 10% business improvements.Proficient in data manipulation and analysisEmployed Python libraries (Pandas, Numpy, Scipy, Scikit-learn, NLTK) to perform complex data analysis tasks, such as [X].Mastery of Spark technologiesDemonstrated expertise in Spark Core, Spark SQL, and Spark Streaming, creating efficient and scalable data processing solutions.

Data Integration and ETLDeveloped robust data pipelines using SSIS, Apache NiFi, and Kafka to extract, transform, and load data from various sources.Cloud-Based Data SolutionsBuilt Python and Apache Beam programs for data validation in Google Cloud Dataflow. Migrated on-premise data to Azure Data Lake using Azure Data Factory.Big Data ProcessingUtilized Spark, Scala, Dataframes, Spark SQL, and Spark MLlib for large-scale data processing and analysis from RDBMS and streaming sources.Data Warehousing and ModelingDesigned and implemented data warehouses using star and snowflake schemas, optimizing data models for query performance.Data Visualization and ReportingCreated interactive dashboards and reports using Tableau to provide actionable insights.Agile Development and DevOps Implemented Agile methodologies, microservices architecture, and DevOps practices for data projects.Machine Learning and AIDeveloped machine learning models using Python and PySpark for binary prediction and explored NLP techniques for text analysis.

Data Integration and ETL Developed complex SSIS/ETL packages to extract, transform, and load data from various sources.Data Pipeline AutomationUtilized Oozie Scheduler to automate ETL pipelines and orchestrate map reduce jobs.Real-time Data ProcessingIntegrated Kafka with Spark Streaming for real-time data processing.Cloud Infrastructure ManagementManaged AWS security groups, focusing on high-availability, fault-tolerance, and auto-scaling using Terraform templates.Data VisualizationCreated interactive dashboards and reports using Tableau and SAS Visual Analytics.Data Warehousing and ModelingDefined facts, dimensions, and designed data marts using Ralph Kimball's Dimensional Data Mart modeling methodology.Data Querying and AnalysisUtilized Hive SQL, Presto SQL, and Spark SQL for ETL jobs, selecting the appropriate technology for each task.Monitoring and LoggingDefined and deployed monitoring, metrics, and logging systems on AWS.Exception Handling Developed code to handle exceptions and push exceptions to a Kafka topic.Data ValidationPerformed ETL and data validation using SQL Server Integration Services.Data Warehouse OptimizationOptimized and tuned the Redshift environment to improve query performance by up to 100x.Data AutomationDeveloped Python scripts to automate data sampling processes.Machine Learning Applied machine learning algorithms (decision tree, logistic regression, Gradient Boosting Machine) to build predictive models using scikit-learn.Data NormalizationWrote data normalization jobs for new data ingested into Redshift.Ad-Hoc ReportingCreated ad-hoc queries and reports to support business decisions using SQL Server Reporting Services (SSRS).