Data Analyst

SQL Development: Advanced proficiency in querying, manipulating, and optimizing data in relational databases. Data Visualization Tools: Solid experience with tools like Power BI, Tableau, or Looker Studio (formerly Google Data Studio) for creating interactive dashboards and clear reports. Data Analysis & Statistics: Ability to identify trends, patterns, and extract meaningful insights from large volumes of data. Knowledge of statistics applied to business analysis. Python: Ability to use Python (libraries like Pandas, NumPy, Matplotlib, Seaborn) for exploratory data analysis (EDA), data cleaning, and creating scripts for report automation or light processing. Agile Methodologies: Familiarity with agile work methodologies (Scrum, Kanban) to collaborate effectively within teams and respond quickly to changes. Databases: Understanding of different database types (SQL Server, Oracle, MongoDB, etc.) to comprehend how data is stored and structured, and to query it efficiently. Working with AWS (Amazon Web Services): Data Querying and Extraction: Experience using AWS services to access and extract data from Data Lakes (especially S3) and Data Warehouses (like Aurora or Redshift). Monitoring and Analyzing Existing Data Pipelines: Ability to understand data flows (even if not creating them) and use AWS tools to validate the quality and integrity of information entering the Data Lake. Using Services like: S3 (for accessing raw and processed data), Aurora or DynamoDB (for querying transactional data), and possibly familiarity with the output of Glue or Kinesis (to understand data provenance).