* Data architecture design and development: Creating and maintaining scalable and efficient data architectures for processing and storing large volumes of data.* Extraction, Transformation, and Loading (ETL): Collecting data from various sources, transforming it into a suitable format, and loading it into big data storage and processing systems.* Implementation of data pipelines: Designing and developing data pipelines for the continuous flow of information from multiple sources to storage and analysis systems.* Performance optimization: Making adjustments and optimizations in big data processing systems to improve performance and efficiency in handling large datasets.* Data quality assurance: Implementing strategies and processes to ensure the quality and consistency of data used in big data projects.* Requirements analysis and solution design: Collaborating with cross-functional teams to understand project requirements and design big data engineering solutions that meet business needs.* Data security and governance: Implementing data security measures and governance policies to protect the integrity and confidentiality of information stored in big data systems.* Monitoring and issue resolution: Monitoring and overseeing big data systems to identify potential issues or bottlenecks and taking corrective actions when necessary.

* Technical Problem Solving: Addressing a variety of issues related to software, and networks.* Customer Service: Interacting with clients to understand their needs and provide effective solutions.* Preventive Maintenance: Executing updates and patches to maintain system stability and security.* Documentation: Maintaining detailed records to streamline efficient issue resolution.* Training: Providing guidance on the proper use of products and common problem-solving techniques.* Internal Collaboration: Collaborating within teams to address technical challenges and improve products and services.

* Data processing: Collecting, cleaning, and processing large datasets for further analysis.* Data visualization: Creating charts, tables, and other visual elements to effectively represent the results of data analysis, aiding in comprehension and decision-making.* Collaboration with cross-functional teams: Working closely with professionals from different areas such as software engineers, data scientists, business analysts, and project leaders to ensure the quality and consistency of results.* Database maintenance and updates: Ensuring that databases are up-to-date and accurate, performing regular maintenance tasks such as performance optimization and troubleshooting.* Continuous improvement: Identifying opportunities for improvement in processes, tools, and techniques used, and proposing innovative solutions to optimize efficiency and quality of work.