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 Quality and System Integration Challenges
Focuses on data integrity, governance, and the operational issues that arise when data moves between systems. Candidates should be able to identify common data quality problems such as duplicates, missing or inconsistent fields, formatting mismatches, schema drift, and validation gaps. Understand how those issues propagate through integration pipelines and impact reporting, analytics, forecasting, and other downstream processes. Discuss reconciliation strategies, validation rules, data cleansing, deduplication, master data management patterns, monitoring and alerting for data anomalies, and policies for schema evolution and versioning. Also cover practical approaches to prevent and remediate integration induced data errors and how to prioritize data quality work across cross-system business workflows (for example, CRM/billing integrations, HR and compensation data feeds, marketing automation pipelines, or product analytics), not just any single business function.
Data Integration and Flow Design
Design how systems exchange synchronize and manage data across a technology stack. Candidates should be able to map data flows from collection through activation, choose between unidirectional and bidirectional integrations, and select real time versus batch synchronization strategies. Coverage includes master data management and source of truth strategies, conflict resolution and reconciliation, integration patterns and technologies such as application programming interfaces webhooks native connectors and extract transform load processes, schema and field mapping, deduplication approaches, idempotency and retry strategies, and how to handle error modes. Operational topics include monitoring and observability for integrations, audit trails and logging for traceability, scaling and latency trade offs, and approaches to reduce integration complexity across multiple systems. Interview focus is on integration patterns connector trade offs data consistency and lineage and operational practices for reliable cross system data flow.
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 Cleaning and Business Logic Edge Cases
Covers handling data centric edge cases and complex business rule interactions in queries and data pipelines. Topics include cleaning and normalizing data, handling nulls and type mismatches, deduplication strategies, treating inconsistent or malformed records, validating results and detecting anomalies, using conditional logic for data transformation, understanding null semantics in SQL, and designing queries that correctly implement date boundaries and domain specific business rules. Emphasis is on producing robust results in the presence of imperfect data and complex requirements.
Data Integration and Extract, Transform, Load
Design and operation of data flows between systems, including extract, transform, load (ETL/ELT) pipelines, API integrations and webhooks, schema mapping, data validation, deduplication and reconciliation, error handling, retry and idempotency patterns, monitoring and observability, throughput and latency considerations, and testing strategies. Covers practical approaches to batching versus streaming, transformation patterns, mapping identity across systems (for example matching records across a CRM, data warehouse, or third-party API), and building robust instrumentation and alerts to detect and resolve data issues. Applies broadly to integrating data between any pair of internal or external systems, not limited to one product area.