Data Analyst

Data and artificial intelligence hold transformative potential for emergency management-accelerating disaster response, improving resource allocation, detecting fraud, and scaling human capacity during surge operations. As a Senior Data Analyst on our FEMA ITSA 3.0 program, you will lead the enterprise-wide assessment of FEMA’s data readiness and responsible AI/automation opportunities, identifying where intelligent systems can deliver measurable mission value while meeting rigorous governance, privacy, and oversight requirements.

You’ll evaluate data quality, accessibility, governance maturity, and interoperability across FEMA’s portfolio to understand what’s possible today and what foundational improvements are needed to unlock advanced analytics and AI capabilities tomorrow. Your analysis will go beyond technical feasibility-you’ll define mission-focused use cases that connect data science to operational outcomes like workload triage during disasters, surge support automation, fraud prevention, and predictive analytics for resource planning. This role places you at the intersection of data strategy, AI ethics, and mission impact, where your expertise will shape how FEMA responsibly leverages data and automation to serve disaster-affected communities more effectively. Your recommendations will inform enterprise investment in data platforms, governance frameworks, and AI-enabled capabilities that balance innovation with accountability.

Responsibilities:

Data Quality & Readiness Assessment:

Evaluate data quality dimensions including accuracy, completeness, consistency, timeliness, and validity across FEMA’s systems

Assess data accessibility, discoverability, and usability for analytics and operational decision-making

Document data dictionaries, metadata repositories, and data cataloging maturity

Review data lineage, provenance tracking, and data flow documentation

Identify data gaps, quality issues, and structural constraints that limit analytics readiness

Assess data volume, velocity, variety, and veracity characteristics relevant to AI/ML applications

Data Architecture & Interoperability Analysis:

Map data exchanges, upstream/downstream dependencies, and cross-system data flows

Assess interoperability constraints including incompatible data models, inconsistent semantics, and format mismatches

Identify duplicative data entry, redundant data storage, and opportunities for single-source-of-truth architectures

Review undocumented interfaces, shadow data sharing, and informal data exchange patterns

Evaluate API maturity for data access and standards-based integration patterns (RESTful APIs, event streaming, data virtualization)

Recommend standards-based exchange patterns including common data elements, shared vocabularies, and enterprise data models

Data Governance & Stewardship Evaluation:

Assess data governance maturity including policies, standards, roles, and accountability structures

Review data stewardship practices, data ownership clarity, and cross-functional governance forums

Evaluate metadata management, business glossary implementation, and semantic consistency

Assess master data management approaches and reference data governance

Review data quality monitoring, issue remediation workflows, and continuous improvement processes

Evaluate alignment with federal data strategy principles and open data requirements

AI/Automation Use Case Definition & Feasibility Analysis:

Define compelling analytics and automation use cases aligned to FEMA mission priorities and operational pain points

Develop AI/automation scenarios such as:

o Workload triage and prioritization during disaster surge operations

o Intelligent routing and case assignment to optimize resource allocation

o Fraud detection and prevention in grants and assistance programs

o Predictive analytics for disaster forecasting, resource pre-positioning, and demand planning

o Natural language processing for survivor communication, document processing, and sentiment analysis

o Computer vision for damage assessment, geospatial analysis, and infrastructure evaluation

o Robotic process automation (RPA) for repetitive administrative tasks

Identify enabling datasets, required data pipelines, feature engineering needs, and model development requirements

Assess technical feasibility including data sufficiency, computational requirements, and integration complexity

Quantify mission value and operational benefits in measurable terms (time savings, accuracy improvement, capacity augmentation)

Responsible AI, Risk & Oversight Framework Development:

Outline governance structures for responsible AI including ethical review boards, bias assessment processes, and human-in-the-loop controls

Define privacy controls, personally identifiable information (PII) protections, and privacy-enhancing technologies applicable to AI systems

Address records management requirements for AI-generated decisions and algorithmic transparency

Establish risk controls including fairness testing, bias mitigation, explain ability requirements, and adversarial robustness

Develop accountability structures including roles, responsibilities, and decision authority for AI system oversight

Recommend model validation, performance monitoring, and continuous evaluation frameworks

Address algorithmic transparency, explain ability requirements, and human review mechanisms for high-stakes decisions

Bachelor’s degree and 15+ years of experience in analytics, data engineering, data architecture, or AI/ML solution architecture within regulated or government environments

Demonstrated expertise with data governance frameworks, metadata management practices, and enterprise data strategy development

Proven experience with standards-based integration approaches including RESTful APIs, event-driven architectures, and data exchange standards

Ability to translate mission scenarios and operational requirements into measurable analytics outcomes and AI use cases

Strong understanding of responsible AI principles including fairness, accountability, transparency, ethics, and bias mitigation

Experience managing risk and oversight requirements in data and AI implementations within compliance-heavy environments

Exceptional analytical skills with the ability to assess complex data landscapes and identify strategic opportunities

Strong communication skills; ability to articulate technical data concepts to non-technical mission stakeholders and executives

Ability to obtain DHS Entry on Duty (EOD) clearance

Desired:

Familiarity with FEMA Enterprise Data Warehouse (EDW), Operational Data Store (ODS), or FEMA mission systems and data architectures

Experience enabling cross-agency data interoperability, federal data sharing, or state/local data integration

Exposure to MLOps practices, model lifecycle management, and AI operations platforms

Knowledge of responsible AI frameworks from NIST, OMB, or industry standards bodies

Experience with federal privacy frameworks including Privacy Impact Assessments (PIAs) and System of Records Notices (SORNs)

Background in emergency management, disaster response, grants management, or social safety net program data

Hands-on experience with cloud data platforms (AWS, Azure) including data lakes, warehouses, and analytics services

Proficiency with data science tools and platforms (Python, R, SQL, Spark, Databricks, SageMaker, Azure ML)

Experience with data visualization and business intelligence platforms (Tableau, Power BI, Qlik)

Knowledge of geospatial data, GIS systems, and location intelligence relevant to emergency management

Familiarity with natural language processing, computer vision, or other AI/ML domain applications

Understanding of federal data governance policies including Federal Data Strategy, OPEN Government Data Act, and CDO Council guidance

Background in data ethics, algorithmic fairness assessment, or AI explain ability research

Certifications in data management (CDMP), data governance, or cloud data platforms

There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.

Since this position can be worked in more than one location, the range shown is the national average for the position.

The proposed salary range for this position is: $105,100-$231,100

At CACI, we place character and innovation at the center of everything we do. As a valued team member, you’ll be part of a high-performing group dedicated to our customer’s missions and driven by a higher purpose - to ensure the safety of our nation.

An environment of trust.

CACI values the unique contributions that every employee brings to our company and our customers - every day. You’ll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.

A focus on continuous growth.

Together, we will advance our nation’s most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground - in your career and in our legacy.