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).
Data Observability and Governance
Encompasses designing monitoring, alerting, governance, and metadata practices to maintain long term data reliability. Topics include building observability for data pipelines with logging metrics and traces, setting service level agreements and data quality service level indicators, anomaly detection for data and metrics, automated validation and alerting, lineage and provenance tracking, metadata and cataloging, data contracts, access controls for sensitive data, and processes for governance and compliance. Candidates should be able to design end to end frameworks that combine validation checks, anomaly detection, monitoring dashboards, incident workflows, and documentation to ensure trust in data products.
Data Quality and Database Management
Principles and practices for ensuring clean, accurate, and well governed databases and data systems. Covers data hygiene techniques such as deduplication, validation rules, field standardization, regular audits, record merging, archival policies, and remediation workflows. Includes data governance topics like data ownership, stewardship, policy definition, documentation, privacy and compliance controls, and role based access. Addresses how poor data quality propagates downstream into reporting, analytics, personalization, and business decision making, and how to trace root causes across ingestion, transformation, and storage layers. Candidates should be able to diagnose common integrity issues (duplicates, stale or missing fields, schema drift, broken foreign keys), propose tooling and process solutions, and explain how to operationalize data quality and governance at scale across an organization's data systems.
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.