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 Lake and Warehouse Architecture
Designing scalable data platforms for analytical and reporting workloads including data lakes, data warehouses, and lakehouse architectures. Key topics include storage formats and layout including columnar file formats such as Parquet and table formats such as Iceberg and Delta Lake, partitioning and compaction strategies, metadata management and cataloging, schema evolution and transactional guarantees for analytical data, and cost and performance trade offs. Cover ingestion patterns for batch and streaming data including change data capture, data transformation approaches and compute engines for analytical queries, partition pruning and predicate pushdown, query optimization and materialized views, data modeling for analytical workloads, retention and tiering, security and access control, data governance and lineage, and integration with business intelligence and real time analytics. Also discuss operational concerns such as monitoring, vacuuming and compaction jobs, metadata scaling, and strategies for minimizing query latency while controlling storage cost.
Cloud Data Architecture and Tradeoffs
Designing data architectures specifically for cloud environments and evaluating platform trade offs. Topics include when to use managed relational services, managed nonrelational services, cloud data warehouses, cloud object storage, lifecycle policies, cross region replication, data residency and compliance considerations, cost versus performance trade offs, managed service operational constraints, and strategies for high availability and disaster recovery in the cloud. Candidates should be able to compare cloud service options and justify choices based on reliability, cost, and compliance.
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.
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.
Data Collection and Instrumentation
Designing and implementing reliable data collection and the supporting data infrastructure to power analytics and machine learning. Covers event tracking and instrumentation design, decisions about what events to log and schema granularity, data validation and quality controls at collection time, sampling and deduplication strategies, attribution and measurement challenges, and trade offs between data richness and cost. Includes pipeline and ingestion patterns for real time and batch processing, scalability and maintainability of pipelines, backfill and replay strategies, storage and retention trade offs, retention policy design, anomaly detection and monitoring, and operational cost and complexity of measurement systems. Also covers privacy and compliance considerations and privacy preserving techniques, governance frameworks, ownership models, and senior level architecture and operationalization decisions.