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, Mapping, and Transformation
Understand data quality concepts: completeness, accuracy, consistency, timeliness, and validity. Know how to identify and address data quality issues. Understand data mapping: matching fields across systems, handling different naming conventions, data type conversions, and field transformations. Be familiar with concepts like null value handling, duplicate detection, and data validation rules. Understand that poor data quality cascades through marketing systems.
Data Quality and Database Management
Principles and practices for ensuring clean, accurate, and well governed marketing and customer databases. 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 marketing specific concerns such as CRM best practices, lead routing impacts, personalization accuracy, measurement and attribution implications, and how poor data quality affects analytics and revenue reporting. Candidates should be able to diagnose common integrity issues, propose tooling and process solutions, and explain how to operationalize data quality at scale across marketing and sales systems.
Data Quality and Validation
Covers the core concepts and hands on techniques for detecting, diagnosing, and preventing data quality problems. Topics include common data issues such as missing values, duplicates, outliers, incorrect labels, inconsistent formats, schema mismatches, referential integrity violations, and distribution or temporal drift. Candidates should be able to design and implement validation checks and data profiling queries, including schema validation, column level constraints, aggregate checks, distinct counts, null and outlier detection, and business logic tests. This topic also covers the mindset of data validation and exploration: how to approach unfamiliar datasets, validate calculations against sources, document quality rules, decide remediation strategies such as imputation quarantine or alerting, and communicate data limitations to stakeholders.
Business Intelligence and Reporting Infrastructure
Building and operating reporting and business intelligence infrastructure that supports dashboards, automated reporting, and ad hoc analysis. Candidates should discuss data pipelines and extract transform load processes, data warehousing and schema choices, streaming versus batch reporting, latency and freshness trade offs for real time reporting, dashboard design for different audiences such as individual contributors managers and executives, visualization best practices, data validation and quality assurance, monitoring and alerting for reporting reliability, and governance concerns including access controls and privacy when exposing data.
Data Pipeline and Data Quality
Designing, operating, and optimizing reliable data pipelines and ensuring data quality across ingestion, transformation, and consumption. Covers extract transform load and extract load transform patterns, efficient incremental and batch loading, idempotent processing, change data capture, orchestration and scheduling, and performance tuning to meet service level objectives. Includes data validation strategies such as schema enforcement, null and type checks, range and referential integrity checks, deduplication, handling late arriving and out of order data, reconciliation processes, and data profiling and remediation. Emphasizes observability, monitoring, alerting, and root cause analysis for data quality incidents, as well as data lineage tracking, metadata management, clear ownership and process discipline, testing and deployment practices, and governance to maintain data integrity for analytics and business operations. Also covers data integration concerns across customer relationship management systems, marketing automation systems, reporting systems, and other operational systems, including pipeline error handling, data contracts, and how test and validation checks can be integrated into pipelines to prevent regressions.
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 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 in revenue operations or cross system workflows.
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.
Analytics Architecture and Reporting
Designing and operating end to end analytics and reporting platforms that translate business requirements into reliable and actionable insights. This includes defining metrics and key performance indicators for different audiences, instrumentation and event design for accurate measurement, data ingestion and transformation pipelines, and data warehouse and storage architecture choices. Candidates should be able to discuss data modeling for analytics including semantic layers and data marts, approaches to ensure metric consistency across tools such as a single source of truth or metric registry, and trade offs between query performance and freshness including batch versus streaming approaches. The topic also covers dashboard architecture and visualization best practices, precomputation and aggregation strategies for performance, self service analytics enablement and adoption, support for ad hoc analysis and real time reporting, plus access controls, data governance, monitoring, data quality controls, and operational practices for scaling, maintainability, and incident detection and resolution. Interviewers will probe end to end implementations, how monitoring and quality controls were applied, and how stakeholder needs were balanced with platform constraints.
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 such as nullness and type checks, freshness and timeliness validation, referential integrity, deduplication, outlier detection, reconciliation, and automated alerting. Includes design of 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 including failure handling, recovery time objectives, backup and disaster recovery strategies, observability and incident response for data anomalies. Also covers domain and system specific considerations such as customer relationship management and sales systems: common causes of data problems, prevention strategies like input validation rules, canonicalization, deduplication and training, and business impact on forecasting and operations. Candidates may be evaluated on designing end to end data quality programs, selecting metrics and tooling, defining roles and stewardship, and implementing automated pipelines and governance controls.