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.
Data Quality and Governance
Covers the principles, frameworks, practices, and tooling used to ensure data is accurate, complete, timely, and trustworthy across systems and pipelines. Key areas include data quality checks and monitoring: nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes designing service level agreements for data freshness and accuracy, data lineage and impact analysis, metadata and catalog management, data classification, access controls, and compliance policies. Encompasses operational reliability of data systems: failure handling, recovery time objectives, backup and disaster recovery strategies, data observability, and incident response for data anomalies. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship (data owner, steward, custodian), building golden-record and master-data-management strategies for record linkage and deduplication across source systems (illustrative domains include CRM and sales data, IoT telemetry, financial transactions, and event or log data, among others), and implementing automated pipelines and governance controls.