Sr Data Engineer(SSIS + AWS Glue)

Data Architecture Design : Designing and developing data architectures and data models to support the organization's data needs and analytical requirements. Data Pipeline Development : Building and maintaining robust data pipelines to extract, transform, and load (ETL) data from various sources into data warehouses or data lakes. Data Integration : Integrating data from different systems and sources, including databases, APIs, and streaming data sources. Data Quality and Governance : Ensuring data quality and implementing data governance processes to maintain accurate and consistent data. Big Data Technologies : Working with big data technologies such as Hadoop, Spark, Kafka, and NoSQL databases to process and store large volumes of data. Cloud Services : Utilizing cloud-based data services, such as AWS, Azure, or Google Cloud, for scalable and cost-effective data processing and storage. Performance Optimization : Optimizing data processing and storage to improve performance and efficiency in data operations. Data Security : Implementing data security measures and ensuring compliance with data privacy regulations. Data Monitoring and Troubleshooting : Monitoring data pipelines and systems to identify and resolve data-related issues or performance bottlenecks.