Large Dataset Integration and Modeling Questions

Techniques and best practices for consolidating, cleaning, and modeling large and complex datasets used in financial analysis. Candidates should describe methods to combine data from multiple sources and systems, reconcile and validate inconsistent or missing records, align granularity and time windows, and produce auditable transformation logic. Topics include designing extract transform load processes that scale across markets and years, handling performance and memory constraints, implementing reproducible pipelines with query and scripting languages or cloud tools, and validating outputs through reconciliation tests and data lineage checks. Interviewers assess the ability to balance accuracy, speed, and maintainability when building models that operate at scale.