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Data Engineering & Analytics Infrastructure Topics

Data pipeline design, ETL/ELT processes, streaming architectures, data warehousing infrastructure, analytics platform design, and real-time data processing. Covers event-driven systems, batch and streaming trade-offs, data quality and governance at scale, schema design for analytics, and infrastructure for big data processing. Distinct from Data Science & Analytics (which focuses on statistical analysis and insights) and from Cloud & Infrastructure (platform-focused rather than data-flow focused).

Tracking Systems and Dashboarding

Designing and operating tracking systems and dashboards involves defining meaningful metrics and indicators to represent program health, selecting leading versus lagging measures, instrumenting data collection, and presenting insights tailored to different stakeholder audiences. Candidates should understand how to identify and structure key performance indicators and leading indicators, ensure data quality and reliable pipelines, determine refresh cadence and ownership, design role specific views and visualizations, and implement alerting and escalation rules that minimize noise. Relevant considerations include cost and performance of instrumentation, data governance and access controls, integration with business intelligence and observability tooling, and how dashboards drive decisions across product, engineering and executive stakeholders. Interview questions typically evaluate metric frameworks, visualization choices, interpretation of signals, and how tracking systems influence program prioritization and corrective actions.

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