314,552 interview questions from 6,000+ companies.
Design the core pipeline infrastructure for a new project, with attention to orchestration, data quality, idempotency, and future scale.
Explain how you apply automated testing and CI practices to data pipelines and pipeline releases.
Describe a real production pipeline failure, how you diagnosed and fixed it, and what changes you made around orchestration, quality, and reruns.
Explain how to tune a slow PostgreSQL query that joins several large transaction tables using indexes, join strategy, and partitioning.
Discuss practical experience using Docker and Kubernetes to package, run, and monitor pipeline workloads.
Tests search/indexing integration and design choices for retrieval use cases.
Tests event-driven pipeline design using Kafka and related operational considerations.
Tests database schema design for high-volume read performance at AnaVation.
Tests ability to implement CI/CD for reliable data pipeline delivery.
Tests practical scripting skills for ETL automation and operational tasks.
Tests end-to-end pipeline construction with validation and observability.
Tests familiarity with Python tooling for ETL and data transformations.
Tests Python ETL/processing design for mixed structured and unstructured inputs.
Tests ability to explain containerization value and relate it to deployment at AnaVation.
Tests end-to-end understanding of data movement from storage to UI consumption.
Tests cloud migration planning and execution for data platforms.
Tests architecture choices to balance OLTP and OLAP while maintaining fast reads.
Tests SQL performance tuning using indexes and execution plan analysis.
Tests API design for unstructured text serving to front-end dashboards.
Tests pipeline design for mixed data types in one workflow.
28 total questions
