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 Validation for Analytics
Covers techniques and practices for ensuring the correctness and reliability of analytical outputs, metrics, and reports. Topics include designing and implementing sanity checks and reconciliations, comparing totals across different calculation methods, validating metrics against known baselines or prior periods, testing edge cases and boundary conditions, and detecting and flagging data quality anomalies such as missing expected data, unexplained spikes or drops, and inconsistent values. Includes methods for designing queries and monitoring checks that surface data quality issues, debugging analytical queries and calculation logic to identify errors and root causes, tracing problems back through data lineage and ingestion pipelines, creating representative test datasets and fixtures, establishing metric definitions and versioning, and automating validation and alerting for metrics in production.
Data Quality and Validation
Covers the core concepts and hands on techniques for detecting, diagnosing, and preventing data quality problems. Topics include common data issues such as missing values, duplicates, outliers, incorrect labels, inconsistent formats, schema mismatches, referential integrity violations, and distribution or temporal drift. Candidates should be able to design and implement validation checks and data profiling queries, including schema validation, column level constraints, aggregate checks, distinct counts, null and outlier detection, and business logic tests. This topic also covers the mindset of data validation and exploration: how to approach unfamiliar datasets, validate calculations against sources, document quality rules, decide remediation strategies such as imputation quarantine or alerting, and communicate data limitations to stakeholders.
Scalable Analytics Approach
Describe approaches for designing analyses and reporting that scale across markets, product lines, content types, and customer segments rather than being one off. Topics include parameterization of reports, modular metric definitions, reusable templates, automation of data extraction and transformation, instrumentation for monitoring and data quality, governance of metric definitions, and tradeoffs between generality and specificity. Provide examples of how you reduced repeated manual effort, improved maintainability, and partnered with data engineering to productionize analytics so they support many use cases.
Large Dataset Integration and Modeling
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.
Data Infrastructure and Architecture Experience
A prompt to describe the candidate's hands on experience building and operating data infrastructure. Candidates should be prepared to discuss specific pipelines, ETL or ELT systems, streaming frameworks, data warehouses and lakes, the scale of data processed, tooling and platforms used, performance and cost trade offs they made, monitoring and data quality practices, incidents or scalability challenges they addressed, and measurable outcomes or improvements resulting from their work.