Technical Vision and Infrastructure Roadmap Questions
This topic assesses a candidate's ability to define a multi year technical vision for infrastructure, platform, and systems and to translate that vision into a practical execution roadmap. Core skills include evaluating technology choices and architecture evolution, planning migration and modernization paths, anticipating scalability and capacity needs, and balancing cost performance with resilience and operational reliability. Candidates should demonstrate approaches to managing technical debt, sequencing investments across quarters and releases, estimating resources and timelines, establishing measurable infrastructure goals and key performance indicators, and implementing governance and standards. Discussion may also cover reliability and observability, security and compliance considerations, trade offs between short term stability and long term rearchitecture, prioritization to enable business outcomes, and communicating technical trade offs to both technical and non technical stakeholders.
EasyTechnical
0 practiced
You are a Data Engineer at a mid-stage SaaS company (50M monthly active users). The exec team asked you to write a 3-year technical vision for data infrastructure and platform to enable analytics and ML. Describe the vision: architecture principles, target platforms (cloud/on‑prem/hybrid), core services (ingest, storage, processing, serving, governance), major migrations or cutovers you would anticipate, and high-level KPIs to measure success. Explain key trade-offs briefly.
EasyTechnical
0 practiced
Design a minimal but effective observability stack for ETL pipelines. Include components for logs, metrics, tracing, alerting, dashboards and lineage. Suggest concrete technologies (e.g., Prometheus, Grafana, OpenTelemetry, ELK, Data Catalog) and list what you would instrument on each ETL job (duration, read/write counts, error types, data volume, lineage metadata).
EasyTechnical
0 practiced
Explain SLI, SLO and SLA in the context of data pipelines. Provide example SLIs for batch and streaming jobs (e.g., successful-run-rate, data-freshness latency, end-to-end latency), suggest SLO thresholds and explain what actions to take when SLOs are breached (page, mitigate, schedule postmortem).
MediumTechnical
0 practiced
Design a three-phase rollout plan to achieve GDPR and HIPAA compliance for a multi-tenant data platform. Cover discovery and classification of PII/PHI, access-control and least privilege, consent and data subject request handling, audit logging, encryption, and validation/testing steps including evidence collection.
HardTechnical
0 practiced
Explain progressive migration patterns—strangler fig, dual-write, backfill—for re-architecting data models and pipelines. For each pattern, describe concrete steps, tooling you might use, validation approaches, pitfalls to avoid, and an example scenario where the pattern is the best choice.
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