Data Analyst - Cyber Analytics

Redhawk; Arlington (VA); Hybrid; USD 130,000 - 165,000; Full time

Job summary

Redhawk Federal seeks an experienced Data Analyst- Cyber Analytics to support the FSC Program in the Cyber & Intelligence Sector. On-site in Arlington, VA with a hybrid schedule after 90 days.

You will analyze large cybersecurity datasets, develop dashboards, and implement automated pipelines to deliver actionable insights for mission-critical decisions. The role requires 9+ years of relevant experience (or 7 with a Master's, 4 with a PhD), Python/SQL proficiency, and the ability to communicate

Qualifications

• Bachelor's degree required; 9+ years of relevant experience, or 7 years with a Master's, or 4 years with a PhD.
• Minimum 2 years of hands-on programming with Python and/or SQL.
• Experience creating dashboards/reports with Power BI or Tableau.
• Foundational cybersecurity concepts or telemetry knowledge.
• Ability to communicate findings to both technical and non-technical audiences.

Responsibilities

• Analyze large-scale cybersecurity and operational datasets to identify trends and risks.
• Prepare, clean, transform, and validate data for analysis.
• Develop dashboards and visualizations using Power BI, Tableau, or similar tools.
• Build automated data pipelines and reporting processes to improve data quality.
• Apply statistics and machine learning for anomaly detection and predictive analytics.
• Collaborate with analysts, engineers, and leadership to translate requirements into insights.
• Document data sources, transformations, and methodologies for transparency.

Skills

Python

SQL

Power BI

Tableau

Data visualization

ETL/ELT

Machine learning

Pandas

Threat analytics

Data wrangling

Communication skills

Education

Bachelor's degree in a relevant field

Master's degree (preferred)

PhD (relevant)

Tools

SQL Server

Power BI

Tableau

Job description

Redhawk Federal is seeking an experienced Data Analyst- Cyber Analytics professional to support our Federal Strategic Cyber (FSC) Program within the Cyber & Intelligence Sector.

Location

On-site in Arlington, VA.Hybrid schedule.1 day per week after a 90-day probationary period.

In this role, you'll work with large-scale cybersecurity and operational datasets to uncover trends, identify emerging threats, and deliver actionable insights that support mission-critical decisions. You'll collaborate with cybersecurity engineers, threat analysts, and program leadership to transform complex and often imperfect data into meaningful intelligence that strengthens national security operations.

This is an excellent opportunity for an experienced data professional who enjoys combining data analytics, Python/SQL development, automation, visualization, and applied data science to solve real-world challenges in a high-impact federal environment.

What You'll Do

• Analyze large-scale cybersecurity and operational datasets, including network logs, authentication data, phishing activity, and threat telemetry, to identify trends, anomalies, patterns, and emerging risks.
• Independently explore, clean, transform, validate, and prepare complex or imperfect datasets for analysis based on project and analytical requirements.
• Perform data extraction, validation, cleansing, transformation, aggregation, and enrichment using Python/Pandas and SQL.
• Conduct exploratory data analysis (EDA) to support operational monitoring, situational awareness, and decision-making.
• Develop and maintain dashboards, reports, and visualizations using Power BI, Tableau, or similar platforms.
• Develop automated data pipelines, reporting processes, and analytical workflows to reduce repetitive manual tasks and improve data quality and repeatability.
• Apply statistical and machine learning techniques, where appropriate, to support anomaly detection, classification, pattern identification, and predictive analytics.
• Develop and maintain metrics related to cyber activity, authentication trends, phishing events, network activity, and external threats.
• Collaborate with cybersecurity analysts, engineers, and stakeholders to translate business and operational requirements into analytical solutions and data-driven insights.
• Identify data-quality issues, reporting gaps, inconsistencies, and opportunities for process improvement.
• Document data sources, transformation logic, analytical methodologies, business rules, and reporting processes to support transparency and repeatability.
• Support ad hoc analytical requests and emerging operational requirements.

QUALIFICATIONS

• Bachelor's degree and a minimum of 9 years of relevant experience; 7 years with a master's degree; or 4 years with a PhD.
• An additional four years of relevant experience may be substituted in lieu of the bachelor's degree requirement.
• Minimum 2 years of hands-on programming experience, preferably using Python and/or SQL.
• Demonstrated experience performing data analysis, including data preparation, validation, transformation, and exploratory analysis.
• Demonstrated ability to independently work with complex or imperfect datasets and determine appropriate preparation methods based on the analytical objective.
• Experience developing dashboards and reports using Power BI, Tableau, or similar visualization platforms.
• Foundational knowledge of cybersecurity concepts and/or network and host telemetry.
• Ability to communicate analytical findings clearly to both technical and non-technical audiences.
• Active Top Secret security clearance with the ability to obtain TS/SCI.
• Must possess or be able to obtain one of the following certifications before the start date and maintain it as a condition of employment:
• CASP+/SecurityX
• CCNP Security
• CEH
• CFR
• CHFI
• CISA
• CISSP (or Associate)
• Cloud+
• CND
• CySA+
• GCED
• GCIH
• GICSP
• SSCP

Preferred Qualifications

• Strong hands-on experience with Python, Pandas, and SQL for data transformation, automation, and analytics.
• Security+ certification
• Experience supporting cybersecurity operations, threat analysis, security monitoring, or cyber-related analytics.
• Experience working with large-scale operational, security, or telemetry datasets.
• Experience developing automated reporting pipelines, ETL/ELT processes, or reusable analytical workflows.
• Experience using data visualization to support leadership reporting and operational decision-making.
• Experience with statistical analysis and supervised or unsupervised machine learning.
• Experience developing and evaluating machine learning models using Python libraries such as scikit-learn, including feature engineering, model validation, and performance evaluation.
• Experience working in collaborative analytical environments where requirements evolve based on operational needs.