Below are representative areas and examples to focus your practice. Tailor depth to your target team (HPC/AI, enterprise integrations, or full-stack product).

Coding / Algorithms

Expect practical problems emphasizing correctness, readability, and tests.

• Implement a sliding-window algorithm to detect anomalies in a time series; analyze complexity.
• Design a paginated API client with idempotent retries and exponential backoff.
• Refactor a nested-loop operation to linear or n log n complexity; provide tests.
• Parse and validate large JSON payloads efficiently; handle partial failures.
• Implement a thread-safe in-memory cache with TTL.

System Design / Architecture

Interviewers assess clarity of boundaries, data flow, observability, and secure operations.

• Design a service to synchronize HR data from Workday to downstream systems with auditability and replay.
• Architect a multi-tenant API with rate limiting, caching, and role-based access control.
• Plan a zero-downtime database migration and rollback strategy.
• Propose a logging, metrics, and tracing approach for an event-driven system.
• Design a content delivery API with edge caching and invalidation strategies.

Cloud / DevOps / Security

Demonstrate automation, reliability, and compliance-aware thinking.

• Outline a CI/CD pipeline with unit/integration tests, security scans, and blue/green deploys.
• Secure an API on AWS with IAM roles, KMS encryption, and parameterized secrets.
• Build a cost-aware data processing pipeline; propose monitoring and budgets/alerts.
• Draft a disaster recovery plan with RTO/RPO targets for a critical app.
• Harden a container image and runtime; explain SBOM and supply chain protections.

Data-Intensive & HPC/AI (Role-Dependent)

Show you can support, optimize, and troubleshoot scientific workloads at scale.

• Package and deploy a scientific stack with Spack; manage compiler/MPI variants.
• Optimize a GPU training workflow under SLURM; discuss profiling and scaling.
• Diagnose intermittent MPI job failures; enumerate your troubleshooting steps.
• Containerize a research app for reproducibility (Apptainer/Singularity considerations).
• Integrate XDMoD/Open OnDemand to improve user experience and governance.

Integrations & Enterprise Systems

Focus on reliability, data quality, and lifecycle management of integrations.

• Build a Workday Studio integration; compare Studio vs. EIB; handle errors and reprocessing.
• Design webhook consumers with dead-letter queues and replay.
• Validate and transform XML with XSLT; ensure schema evolution safety.
• Implement a robust audit trail for data changes across systems.
• Manage versioning and backwards compatibility for public APIs.

Behavioral / Leadership

Demonstrate ownership, communication, and bias for collaboration.

• Tell us about a time you stabilized a critical production incident—what changed afterward?
• Describe how you handled ambiguous requirements and aligned stakeholders.
• Share an example of mentoring a teammate or leading a technical workshop.
• Explain a time you pushed back for security or quality and how you gained buy-in.
• Walk us through your documentation approach for a new service (what and why).

This timeline will display the typical stages (e.g., recruiter/HR touchpoint, technical screens, on-site/virtual panel, and decision/offer). Use it to plan your preparation cadence, block focused time for take-home or live exercises, and line up references early. Keep communication tight—confirm logistics, ask clarifying questions about expectations, and reflect learnings between stages.

Deep Dive into Evaluation Areas

Coding and Software Fundamentals

Strong fundamentals are non-negotiable. Expect live coding or take-home work that tests data structures, algorithms, code clarity, and correctness—with emphasis on readability, testing, and real-world constraints.

Be ready to go over:

• Core algorithms and data structures: Arrays, hash maps, trees/graphs, heaps; complexity analysis with pragmatic trade-offs.
• Testing and code quality: Unit tests, property-based testing, meaningful error handling, and logging practices.
• APIs and data modeling: Designing clean endpoints, versioning strategies, pagination, idempotency, and schema evolution.
• Advanced concepts (less common): Concurrency models, streaming I/O, vectorization for scientific Python, memory profiling in C/C++.

Example questions or scenarios:

• "Implement a rate limiter with clear tests and complexity discussion."
• "Given a flaky integration, add resiliency: timeouts, retries with backoff, circuit breakers—explain trade-offs."
• "Refactor this function for readability and testability; add input validation and error semantics."

System Design and Architecture

You will design systems that are secure, scalable, and maintainable—from full-stack web apps to data pipelines and ERP integrations. Interviewers look for clarity of boundaries, data flow, observability, and sustainable operations.

Be ready to go over:

• Service design: Stateless services, session management, caching, and API gateways.
• Data architecture: Relational modeling (PostgreSQL/MySQL), indexing, caching strategies, and analytics pipelines.
• Integration patterns: Webhooks, event-driven architectures, Workday Studio/EIB connectors, retries and DLQs.
• Advanced concepts (less common): Multi-tenant design, zero-downtime migrations, blue/green or canary deployments.

Example questions or scenarios:

• "Design a system to synchronize employee data from Workday to downstream apps with guaranteed delivery and auditability."
• "Architect a content API with role-based access controls, rate limits, and observability (metrics, tracing, logs)."
• "Plan a migration from a monolith to services with minimal downtime; outline rollout and rollback."

Cloud, DevOps, and Security-by-Design

JHU teams operate in AWS and Azure, often with Linux foundations and compliance requirements. You will be assessed on automation, reliability, and secure deployment practices.

Be ready to go over:

• Infrastructure and automation: CI/CD pipelines, containerization, config management (e.g., Ansible), environment parity.
• Cloud services: IAM, secrets management, networking basics (VPCs, subnets, security groups), storage strategies.
• Security and compliance: NIST SP 800-171 alignment, encryption in transit/at rest, least privilege, vulnerability management.
• Advanced concepts (less common): Disaster recovery strategies, cross-account patterns, cost governance for research workloads.

Example questions or scenarios:

• "Design a secure data processing workflow on AWS that meets NIST controls—detail IAM, encryption, and logging."
• "Walk through a CI/CD pipeline for a Python API with automated tests, SAST/DAST, and rollback."
• "Harden a containerized app: base image choice, SBOM, dependency pinning, and runtime restrictions."