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 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 Processing and Transformation
Focuses on algorithmic and engineering approaches to transform and clean data at scale. Includes deduplication strategies, parsing and normalizing unstructured or semi structured data, handling missing or inconsistent values, incremental and chunked processing for large datasets, batch versus streaming trade offs, state management, efficient memory and compute usage, idempotency and error handling, and techniques for scaling and parallelizing transformation pipelines. Interviewers may assess problem solving, choice of algorithms and data structures, and pragmatic design for reliability and performance.