Data Analyst

Qualifications:

• A bachelor's or master's degree in computer science, information technology, data science, or a related field.
• Two (2) to Five (5) years' experience with:
• Data analysis: This can include experience in data profiling, data mining, and statistical analysis, among other areas.
• Proficiency in Power BI: The candidate should have experience working with Power BI's various components and features, including data modeling, DAX functions, report design, and visualization techniques.
• Strong SQL skills: The candidate should have experience in writing SQL queries to extract, transform, and load data into Power BI.
• Knowledge of data warehousing concepts: The candidate should have experience working with data warehousing concepts such as star schema, snowflake schema, and ETL processes.
• Experience with other BI and Reporting Tools: While not always required, experience with other business intelligence tools such as PowerBI, Tableau, QlikView, or SAP BusinessObjects can be beneficial.
• Experience with other Programming tools and languages will be useful: SSRS, C#, Python, .NET, PowerShell, and other modern languages.
• Experience in project management: The candidate should have experience managing their time effectively, prioritizing tasks, and meeting deadlines.
• Communication skills: The candidate should have experience working with business stakeholders, understanding their requirements, and translating them into actionable insights..

Requirements:

• Strong experience with Python.
• Experience working with data manipulation techniques and statistics.
• Experience producing Finished Intelligence products for IC customers.
• Knowledge of computer science concepts, data architecture, and common statistical models.
• Knowledge of all-source intelligence, including SIGINT, and/or HUMINT collection reports.
• Familiarity with cable writing styles and classified client databases, including specific targeting tools.
• Ability to communicate technical findings to non-technical audiences.

Key Skills:

• Data cleaning and preparation.
• Data analysis and exploration.
• Statistical knowledge.
• Creating data visualizations.
• Creating dashboards and reports.
• Writing and communication.
• Domain knowledge.
• Problem solving.