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Technical Background and Skills Questions

Provide a clear, evidence based overview of your technical foundation and demonstrated credibility as a technical candidate. Describe programming and scripting languages, frameworks and libraries, databases and data stores, version control systems, operating systems such as Linux and Windows, server and hardware experience, and cloud platforms including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Explain experience with infrastructure as code tools, containerization and orchestration platforms, monitoring and observability tooling, and deployment and continuous integration and continuous delivery practices. Discuss development workflows, testing strategies, build and release processes, and tooling you use to maintain quality and velocity. For each area, explain the scale and complexity of the systems you worked on, the architectural patterns and design choices you applied, and the performance and reliability trade offs you considered. Give concrete examples of technical challenges you solved with hands on verification details when appropriate such as game engine or platform specifics, and quantify measurable business impact using metrics such as latency reduction, cost savings, increased throughput, improved uptime, or faster time to market. At senior levels emphasize mastery in three to four core technology areas, the complexity and ownership of systems you managed, the scalability and reliability problems you solved, and examples where you led architecture or major technical decisions. Align your examples to the role and product domain to establish relevance, and be honest about gaps and areas you are actively developing.

MediumSystem Design
100 practiced
Design an automated daily reporting pipeline that: ingests events from production systems, stores raw data in a cloud data lake, runs scheduled transformations with dbt, and publishes curated tables to a cloud data warehouse for 1000+ dashboards. Include orchestration, failure/retry handling, observability/alerting, and a deployment strategy that supports safe changes to models.
EasyTechnical
83 practiced
Explain what data lineage is and why it is important for BI. Describe at least two tools or approaches to capture lineage (manual documentation, automated lineage from ETL tools, metadata catalogs) and how you'd use lineage during incident triage or data quality investigations.
HardTechnical
100 practiced
For a dashboard that frequently runs complex joins across many dimension tables, discuss the trade-offs between a normalized star schema and a single wide denormalized reporting table in terms of query latency, storage cost, ETL complexity, and the ability to support ad-hoc analysis. Provide an example scenario and your recommendation with justification.
EasyTechnical
105 practiced
A stakeholder reports a dashboard is slow to load. List a prioritized set of quick diagnostic checks and explain what each check reveals (e.g., data source query times, dashboard queries, viz-level calculations, number of rows returned, filters). What immediate change could you make that often yields the biggest improvement?
MediumTechnical
81 practiced
Explain how containerization (Docker) and orchestration (Kubernetes/ECS) can be used for ETL workers, scheduling tasks, and self-hosted BI services. Discuss stateless vs stateful components, scaling patterns for worker pools, logging/monitoring, and secrets management for credentials.

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