Tools, Frameworks & Implementation Proficiency Topics
Practical proficiency with industry-standard tools and frameworks including project management (Jira, Azure DevOps), productivity tools (Excel, spreadsheet analysis), development tools and environments, and framework setup. Focuses on hands-on tool expertise, configuration, best practices, and optimization rather than conceptual knowledge. Complements technical categories by addressing implementation tooling.
Common Digital Technologies & Solutions
Foundational knowledge of technology categories used in digital transformation: cloud computing basics (SaaS, PaaS, IaaS), enterprise software (ERP, CRM), business intelligence tools, process automation and RPA, cybersecurity and data privacy, AI/machine learning fundamentals, and collaboration tools. You don't need to be a technologist, but should understand what these technologies do and why organizations might adopt them.
Technology Evaluation and Selection
Focuses on evaluating technology options and selecting appropriate platforms or vendors. Key skills include defining business and technical requirements, creating evaluation criteria and decision matrices, running proof of concept trials, assessing total cost of ownership and vendor lock in, validating integration feasibility and operational impact, ensuring security and compliance, planning staged rollouts and migrations, and documenting governance for adoption. Interviewers may probe examples of build versus buy decisions and how pilots were used to de risk technology choices.
Technology Solution Fit Assessment
Evaluating and recommending technology solutions that appropriately address specific process problems and user needs. Candidates should demonstrate a problem first approach: identify the underlying process or user pain points, then map to enabling technologies rather than prescribing tools up front. Discuss concrete options such as automated workflow routing to reduce manual email back and forth, intelligent reminders to prevent requests from languishing in inboxes, integration platforms to connect fragmented systems, mobile access to enable approvals from anywhere, and analytics dashboards to provide visibility. Explain why a particular category of tool or specific platform fits the problem by weighing functionality, cost, implementation complexity, integration needs, security and compliance, and likely user adoption. Highlight trade offs, deployment and maintenance considerations, minimum viable implementations, and how to avoid over engineering while ensuring long term extensibility.
Mobile Platform Knowledge
Understanding the iOS and Android mobile platforms, their development ecosystems, and the practical tradeoffs of native and cross platform approaches. Topics include platform languages and toolchains such as Swift and Objective C for iOS and Kotlin and Java for Android, platform architecture and application lifecycle, user interface frameworks and design guidelines, performance and memory considerations, platform specific security and permission models, testing strategies and device fragmentation, build and release processes including app store distribution and versioning, continuous integration and continuous delivery for mobile, and interoperability with backend services. Candidates should be able to explain when to choose native versus cross platform solutions, discuss debugging and profiling tools, and describe how platform constraints influence design and operational decisions.
Business Intelligence Tool Proficiency
Covers knowledge and hands on skills using enterprise business intelligence tools such as Power BI and Tableau. Candidates should demonstrate the end to end workflow: connecting to diverse data sources including spreadsheets, relational databases, data warehouses, and cloud services; exploring and profiling data to understand schema and quality; and performing data transformation and cleaning using extract transform load processes or built in tool features. Includes building efficient data models with appropriate relationships, hierarchies, and performance minded design, and understanding when to use extracts versus live connections and aggregation strategies. Candidates should be able to create visualizations and interactive dashboards by mapping fields to charts, selecting appropriate chart types, applying filters and parameters, configuring drill down and drill through interactions, and assembling visuals into coherent reports. Covers calculated fields and custom metric creation using expression languages such as Data Analysis Expressions and Tableau table calculations, and awareness of performance implications of complex calculations. Also includes familiarity with differences between paginated reports and interactive dashboards, publishing and sharing workflows, deployment and distribution strategies, governance and access controls including row level security and workspace organization, versioning and refresh scheduling, and basic troubleshooting and optimization techniques. Candidates should be prepared to discuss real projects where they chose visualizations, resolved data quality or performance challenges, iterated on stakeholder feedback, and measured adoption and business impact.