As a Sr. Data Engineer, I am responsible for analyzing the current production state of applications and assessing the impact of new implementations on existing business processes. My role involves designing and implementing complex data solutions on Google Cloud Platform (GCP) using services like Airflow, Dataflow, Pubsub, and BigQuery. I lead development efforts and manage diverse teams to handle considerable data sizes (TB/PB) and high complexity projects. Additionally, I create pipelines for data extraction, transformation, and loading from various sources, develop real-time stream processing applications, and optimize data processing using technologies such as Docker, Kubernetes, and PySpark.

As a Azure Data Engineer, I am responsible for extracting, transforming, and loading data from various source systems to Azure Data storage services using Azure Data Factory, T-SQL, and Azure Data Lake Analytics. Additionally, I ingest data to Azure services such as Azure Data Lake, Azure Storage, Azure SQL, and Azure Data Warehouse, and process data using Azure Databricks. I lead development efforts, manage diverse teams, and implement complex data solutions to handle considerable data sizes and high complexity projects. Moreover, I leverage various tools and technologies such as DBT, Matillion ETL, Kafka, Kinesis, Apache Airflow, PySpark, and AWS services to optimize data processing, perform real-time data integration, and automate infrastructure in cloud environments.

As a AWS Data Engineer, I am responsible for running Spark SQL operations on JSON data, converting it into tabular structures with data frames, and storing/writing the data to Hive and HDFS. Additionally, I develop shell scripts for data ingestion and validation, tune performance of Informatica mappings and sessions, and work with data science tools like R, Python, and TensorFlow integrated with Hadoop. Furthermore, I design and implement ETL workflows using tools like Apache Airflow and Oozie, have familiarity with data visualization tools like Tableau and Power BI, and possess knowledge of machine learning algorithms implemented in Hadoop.

As a AWS Data Engineer, I am responsible for creating data pipelines and ETL processes to load data from AWS S3 to RDS and Redshift, utilizing various AWS services like EC2, S3, Lambda, and DynamoDB. Additionally, I provide technical solutions on MS Azure HDInsight, Azure SQL Data Warehouse, and implement data migration techniques using BCP, Azure Data Factory. I also develop Python scripts to automate AWS S3 operations, work with PostgreSQL and MySQL databases, manage data using Panda data frames, and schedule jobs in Autosys. Furthermore, I design generic and standard ETL processes, document ETL strategies, and develop ETL pipelines using Python and SQL for various source systems like SQL Server, MySQL, PostgreSQL, and CSV files, leveraging PySpark for data transformation.

As a SQL BI Developer, I am responsible for developing, improving, and scaling processes for data management and analytics, collaborating with project management to provide accurate forecasts and reports, and handling testing and maintenance in development and production environments. Additionally, I have extensive experience in designing, developing, and implementing ETL processes for various data sources using tools such as Informatica, Talend, and SSIS. I am proficient in both batch processing and real-time streaming ETL methods, and have hands-on experience with technologies such as Apache Spark, Pig, Hive, Sqoop, and Hadoop ecosystem tools for data processing, analysis, and troubleshooting.