CACI Data Analyst Case Interview – Mission Readiness Data Integration and Insight Presentation

This case mirrors CACI’s mission-focused, government customer environment and tests how you analyze and communicate insights in a secure, compliance-conscious setting. You’ll work with a simulated DoD program dataset reflecting logistics, maintenance, and personnel readiness. The panel (typically a hiring manager, senior analyst, and program lead) evaluates how you clarify ambiguous requirements, build defensible metrics, and brief non-technical stakeholders. What you receive: (1) A short customer brief with objectives and constraints (e.g., treat data as CUI-like; assume limited internet/secure enclave workflows). (2) CSV extracts: maintenance_log.csv, work_orders.csv, inventory.csv, personnel_roster.csv, missions.csv, plus a minimal data dictionary. (3) A prompt to outline an MVP dashboard suitable for a federal PMO. What you do: (a) Requirements triage (5 min): confirm mission KPIs (e.g., equipment readiness rate, MTBF, backlog aging), consumers, refresh cadence, and role-based access. (b) Data wrangling (20 min): sketch SQL or pseudo-SQL to clean IDs, deduplicate, normalize unit codes, and join tables; compute KPIs using joins/window functions; flag data quality issues and outliers. (c) Analytics and visualization (20 min): propose a simple Power BI/Tableau storyboard (readiness trend, drivers, drill-through to unit/equipment), a lightweight star schema, and row-level security by unit/role. (d) Risk/governance (10 min): discuss handling of PII/CUI, lineage, validation checks, change control, and working in GovCloud/secure networks; note assumptions and tradeoffs. (e) Executive brief (10 min): present recommendations, defend methodology, and outline a 2–3 sprint delivery plan. How you’re assessed (aligned to CACI norms): mission orientation and customer empathy; structured problem solving; SQL/Python (optional) rigor; data governance and security awareness (NIST/FedRAMP concepts at a practical level); clear communication to federal stakeholders; and bias toward reliable, minimally viable delivery under constraints.