AWS Quicksight

Technical Skills

AWS QuickSight Expertise:

Proficient in building interactive dashboards and visualizations.

Skilled in using QuickSight to connect to various data sources within and outside AWS (e.g., Amazon S3, RDS, Redshift, Athena).

Experienced in QuickSight administration, including managing user access and security.

Python Programming:

Strong programming skills in Python, with a focus on data analysis libraries such as Pandas, NumPy, Matplotlib, and SciPy.

Experience in using Python for data manipulation, cleaning, and preprocessing.

Data Engineering and ETL Processes:

Ability to design and implement robust ETL pipelines using AWS services like AWS Glue, Lambda, and Data Pipeline.

Experience in handling large-scale data storage and computation frameworks.

Database Management:

Familiarity with SQL and NoSQL databases, including performance optimization and schema design.

Ability to write complex SQL queries and integrate them with QuickSight and Python applications.

Machine Learning:

Knowledge of applying machine learning models using Python (e.g., using scikit-learn, TensorFlow, or Keras).

Understanding of integrating predictive analytics and ML insights into business intelligence platforms like QuickSight.

Soft Skills

Project Management:

Strong project management skills with the ability to lead data analytics projects.

Experience in Agile methodologies and sprint planning.

Leadership and Team Management:

Proven ability to lead and mentor a team of analysts and developers.

Excellent communication and interpersonal skills to effectively convey insights and recommendations to non-technical stakeholders.

Problem-Solving:

Strong analytical and problem-solving abilities, with a keen eye for detail and a commitment to high-quality data-driven decision-making.

Innovative Thinking:

Capacity to innovate and integrate new technologies or analytical techniques into existing data workflows.