Oracle Engineering Data Analyst Case Interview (OCI & Autonomous Database Focus)

This live, 60-minute case simulates an Oracle Cloud Infrastructure (OCI) analytics scenario and reflects Oracle’s methodical, SQL-first interview style. You’ll analyze usage and cost telemetry from OCI (e.g., Compute, Block Volumes, Object Storage) and customer adoption signals from an Autonomous Database-backed application. The panel (typically a hiring manager plus a senior analyst and/or data engineer) evaluates how you frame ambiguous enterprise problems, write performant Oracle SQL, and translate findings into pragmatic recommendations for product, finance, and SRE stakeholders. What you’ll do: 1) Problem framing (define KPIs like active tenancies, cost per compartment, storage egress, and cohort retention by service; clarify tenancy/compartment hierarchies and tagging). 2) Hands-on querying (joins, window functions, CTEs, conditional aggregation, date/time handling, and partition awareness) against Oracle-style schemas; discuss query plans, indexing/partitioning trade-offs, and how you’d optimize on Autonomous Database. 3) Insight generation (identify drivers of cost spikes, underutilized resources, and adoption bottlenecks across regions; propose actions such as autoscaling policies, lifecycle policies for Object Storage, or moving workloads to ADB with serverless autoscaling). 4) Data quality and governance (handle PII safely, respect row-level access, and explain how Oracle IAM, tags, and policies support least privilege). 5) Communication (present a concise narrative for a VP audience and a deeper technical appendix for engineers). Focus areas mirror real Oracle cases: deep SQL competency in an Oracle environment, comfort with large, messy enterprise datasets, cloud cost and performance analytics, metrics design for Fusion/OCI products, and crisp, customer-first storytelling. Expect structured prompts, limited but realistic schema documentation, and follow-up what-if questions to test judgment, rigor, and ownership.