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).
Real Time and Batch Ingestion
Focuses on choosing between batch ingestion and real time streaming for moving data from sources to storage and downstream systems. Topics include latency and throughput requirements, cost and operational complexity, consistency and delivery semantics such as at least once and exactly once, idempotent and deduplication strategies, schema evolution, connector and source considerations, backpressure and buffering, checkpointing and state management, and tooling choices for streaming and batch. Candidates should be able to design hybrid architectures that combine streaming for low latency needs with batch pipelines for large backfills or heavy aggregations and explain operational trade offs such as monitoring, scaling, failure recovery, and debugging.
Data Quality & Troubleshooting Missing/Incorrect Data
Understand how to identify and troubleshoot data quality issues. Common issues: (1) Duplicate records—same person appears multiple times in database, (2) Missing data—required fields are blank, (3) Incorrect data—email addresses formatted inconsistently, (4) Out-of-sync data—CRM and analytics show different numbers, (5) Tracking failures—events not being recorded. When investigating data quality issues: (1) What specifically is wrong? (2) How much data is affected? (3) When did it start? (4) What changed around that time? (5) What's the impact? (6) How do we fix it going forward? Example: 'Our lead count from website forms dropped 30% overnight. I checked: Was form code broken? (no) Were people still submitting? (yes) Were submissions being captured? (no—tracked in analytics but not reaching CRM) Root cause: API integration failed. We manually synced overnight data and fixed the API.' For junior level, show you think systematically about investigating issues and involve technical teams when needed.
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 Pipeline Scalability and Performance
Design data pipelines that meet throughput and latency targets at large scale. Topics include capacity planning, partitioning and sharding strategies, parallelism and concurrency, batching and windowing trade offs, network and I O bottlenecks, replication and load balancing, resource isolation, autoscaling patterns, and techniques for maintaining performance as data volume grows by orders of magnitude. Include approaches for benchmarking, backpressure management, cost versus performance trade offs, and strategies to avoid hot spots.
Data Quality Debugging and Root Cause Analysis
Focuses on investigative approaches and operational practices used when data or metrics are incorrect. Includes techniques for triage and root cause analysis such as comparing to historical baselines, segmenting data by dimensions, validating upstream sources and joins, replaying pipeline stages, checking pipeline timing and delays, and isolating schema change impacts. Candidates should discuss systematic debugging workflows, test and verification strategies, how to reproduce issues, how to build hypotheses and tests, and how to prioritize fixes and communication when incidents affect downstream consumers.
Marketing Analytics and Reporting Architecture
Design and implementation of marketing focused analytics and reporting systems. Includes creating tracking plans and event schemas, instrumenting event based analytics, setting up tag management, identity resolution and user stitching, attribution modeling, campaign and funnel measurement, connecting analytics tools to data warehouses, selecting and integrating analytics platforms and visualization tools, designing dashboards for marketing and sales stakeholders, ensuring data quality and consistency for campaign measurement, and operationalizing reporting for optimization and experimentation.
Data Transformation and Loading
Focuses on the extract transform load and extract load transform approaches for ingesting transforming and loading data. Candidates should understand three core stages: extract which is acquiring data from sources such as application programming interfaces databases logs and message queues; transform which is cleaning validating reshaping aggregating and enriching data to meet downstream requirements; and load which is writing processed data to targets such as analytic databases data warehouses data lakes or reporting systems. Topics include the differences between extract transform load and extract load transform, incremental loads versus full refresh, scheduling and orchestration best practices, tooling and frameworks used for transformation and orchestration, idempotency and deduplication strategies, error handling and retry semantics, data quality checks end to end validation recovery and integration with business intelligence and analytics consumers. Interview focus is on concrete transformation logic pipeline orchestration and validation strategies and on choosing the right pattern and tooling for given constraints.
Analytics Platforms and Dashboards
Comprehensive knowledge of analytics platforms, implementation of tracking, reporting infrastructure, and dashboard design to support marketing, product, and content decisions. Candidates should be able to describe tool selection and configuration for platforms such as Google Analytics Four, Adobe Analytics, Mixpanel, Amplitude, Tableau, and Looker, including the trade offs between vendor solutions, native platform analytics, and custom instrumentation. Core implementation topics include defining measurement plans and event schemas, event instrumentation across web and mobile, tagging strategy and data layer design, Urchin Tracking Module parameter handling and cross domain attribution, conversion measurement, and attribution model design. Analysis and reporting topics include funnel analysis, cohort analysis, retention and segmentation, key performance indicator definition, scheduled reporting and automated reporting pipelines, alerting for data anomalies, and translating raw metrics into stakeholder ready dashboards and narrative visualizations. Integration and governance topics include data quality checks and validation, data governance and ownership, exporting and integrating analytics with data warehouses and business intelligence pipelines, and monitoring instrumentation coverage and regression. The scope also covers channel specific analytics such as search engine optimization tools, social media native analytics, and email marketing metrics including delivery rates, open rates, and click through rates. For junior candidates, demonstration of fluency with one or two tools and basic measurement concepts is sufficient; for senior candidates, expect discussion of architecture, pipeline automation, governance, cross functional collaboration, and how analytics drive experiments and business decisions.