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.
Metric Definition and Implementation
End to end topic covering the precise definition, computation, transformation, implementation, validation, documentation, and monitoring of business metrics. Candidates should demonstrate how to translate business requirements into reproducible metric definitions and formulas, choose aggregation methods and time windows, set filtering and deduplication rules, convert event level data to user level metrics, and compute cohorts, retention, attribution, and incremental impact. The work includes data transformation skills such as normalizing and formatting date and identifier fields, handling null values and edge cases, creating calculated fields and measures, combining and grouping tables at appropriate levels, and choosing between percentages and absolute numbers. Implementation details include writing reliable structured query language code or scripts, selecting instrumentation and data sources, considering aggregation strategy, sampling and margin of error, and ensuring pipelines produce reproducible results. Validation and quality practices include spot checks, comparison to known totals, automated tests, monitoring and alerting, naming conventions and versioning, and clear documentation so all calculations are auditable and maintainable.
Experimentation Platforms and Infrastructure
Addresses the technical and organizational infrastructure required to run experiments at scale. Topics include randomization and assignment strategies, traffic allocation, instrumentation and metric collection pipelines, experiment configuration and rollout systems, experiment tracking and metadata, data quality and monitoring, guardrails to detect interference or contamination, automated validity checks, self service experimentation tooling, governance and permissions, and approaches to scale experimentation across many teams while preserving statistical validity. Senior conversations include designing experiment platforms, enabling self service and observability, and trade offs when scaling experiment velocity across products.