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 and Edge Case Handling
Practical skills and best practices for recognizing, preventing, and resolving real world data quality problems and edge cases in queries, analyses, and production data pipelines. Core areas include handling missing and null values, empty and single row result sets, duplicate records and deduplication strategies, outliers and distributional assumptions, data type mismatches and inconsistent formatting, canonicalization and normalization of identifiers and addresses, time zone and daylight saving time handling, null propagation in joins, and guarding against division by zero and other runtime anomalies. It also covers merging partial or inconsistent records from multiple sources, attribution and aggregation edge cases, group by and window function corner cases, performance and correctness trade offs at scale, designing robust queries and pipeline validations, implementing sanity checks and test datasets, and documenting data limitations and assumptions. At senior levels this expands to proactively designing automated data quality checks, monitoring and alerting for anomalies, defining remediation workflows, communicating trade offs to stakeholders, and balancing engineering effort against business risk.
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.
Test Result Storage and Querying
Design storage and query systems for test results and historical test execution data to support trend analysis, flakiness detection, debugging, and reporting. Cover data model choices and trade offs between normalized and denormalized schemas, selection of storage backends such as relational databases document stores time series stores or object storage, ingestion patterns including batch and streaming, partitioning and indexing strategies for efficient queries, query patterns for common use cases such as per test history per build rollups and flaky test detection, retention and archival policies, compression and cost trade offs, linking results to builds commits and test metadata, application programming interface design for result retrieval, data privacy and access control, monitoring and alerting on ingestion and query pipelines, and considerations for scalability latency and maintainability.
Test Result Storage and Analysis
Design systems that ingest, store, index, and analyze large volumes of automated test results and related metadata to support fast queries, pattern detection, and stakeholder reporting. Discuss ingestion strategies such as streaming and batched pipelines, data models for test runs and artifacts, indexing and partitioning to support common query patterns, and tiered storage between fast hot stores and long term archives. Explain trade offs between storage technologies such as time series databases, columnar analytics stores, search engines, object storage, and relational databases with respect to query latency, cost, and retention. Cover aggregation and rollup strategies, anomaly detection and failure pattern identification, linking results to code commits and builds, application programming interfaces for access and export, multi tenant access control, retention and backup policies, and operational concerns such as compaction, cost optimization, and scaling to millions of daily test results. Describe how dashboards and automated alerts surface trends and how the system supports root cause analysis and stakeholder reporting at scale.