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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.

MediumTechnical
0 practiced
Explain how you would implement row-level security (RLS) for a Power BI or Tableau deployment that must enforce per-user access to financial data. Compare implementing RLS at the BI layer vs at the database layer and discuss performance, maintainability, and auditability trade-offs.
HardTechnical
0 practiced
Design a secure data protection architecture for BI pipelines handling PII: include encryption (at-rest/in-transit), key management (KMS integration and rotation), tokenization or hashing strategies, role-based access controls, logging/auditing, and approaches to provide masked data to analysts while enabling meaningful analytics.
MediumSystem Design
0 practiced
You need to build observability for data pipelines. List the key metrics and logs you would capture (e.g., pipeline duration, row counts, data freshness, error rates), describe alerting strategy (thresholds, suppression, escalation), and specify tooling choices (Datadog, Prometheus, Monte Carlo, open-source) and how you'd onboard a new pipeline into monitoring.
MediumTechnical
0 practiced
When and why would you denormalize OLTP-style normalized data into wide denormalized tables for reporting? Describe concrete trade-offs in storage, freshness, update complexity, and query latency, and provide a short example where denormalization meaningfully improved dashboard performance.
MediumSystem Design
0 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.

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