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 and Anomaly Detection
Focuses on identifying, diagnosing, and preventing data issues that produce misleading or incorrect metrics. Topics include spotting duplicates, missing values, schema drift, logical inconsistencies, extreme outliers caused by instrumentation bugs, data latency and pipeline failures, and reconciliation differences between sources. Covers validation strategies such as data tests, checksums, row counts, data contracts, invariants, and automated alerting for quality metrics like completeness, accuracy, and timeliness. Also addresses investigation workflows to determine whether anomalies are data problems versus true business signals, documenting remediation steps, and collaborating with engineering and product teams to fix upstream causes.
Data Cleaning and Business Logic Edge Cases
Covers handling data centric edge cases and complex business rule interactions in queries and data pipelines. Topics include cleaning and normalizing data, handling nulls and type mismatches, deduplication strategies, treating inconsistent or malformed records, validating results and detecting anomalies, using conditional logic for data transformation, understanding null semantics in SQL, and designing queries that correctly implement date boundaries and domain specific business rules. Emphasis is on producing robust results in the presence of imperfect data and complex requirements.
AWS Data Services
Specialized knowledge of Amazon Web Services targeted at data storage, processing, analytics, and streaming. This covers object storage and data lake design with Simple Storage Service including storage classes, lifecycle and partitioning strategies; analytics and warehousing with Redshift including columnar storage, distribution styles, compression, query optimization and concurrency considerations; big data processing with Elastic MapReduce for managed Spark and Hadoop clusters and associated tuning; serverless extract transform and load using Glue and data catalog concepts, schema management and job orchestration; and real time data ingestion and processing with Kinesis including producers, shards, retention, consumers, and stream processing patterns. Candidates should understand when to choose batch versus streaming architectures, how to integrate services into end to end data pipelines, trade offs around scalability, latency, consistency, security, data governance and cost optimization, and monitoring and debugging techniques for data workloads.
Data Manipulation and Transformation
Encompasses techniques and best practices for cleaning, transforming, and preparing data for analysis and production systems. Candidates should be able to handle missing values, duplicates, inconsistency resolution, normalization and denormalization, data typing and casting, and validation checks. Expect discussion of writing robust code that handles edge cases such as empty datasets and null values, defensive data validation, unit and integration testing for transformations, and strategies for performance and memory efficiency. At more senior levels include design of scalable, debuggable, and maintainable data pipelines and transformation architectures, idempotency, schema evolution, batch versus streaming trade offs, observability and monitoring, versioning and reproducibility, and tool selection such as SQL, pandas, Spark, or dedicated ETL frameworks.
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 and Technical Strategy Alignment
Assess how the candidates technical experience and perspective align with the companys data strategy, infrastructure, and product architecture. Candidates should demonstrate knowledge of the companys scale, data driven products, and technical tradeoffs, and then explain concretely how their past work, tools, and approaches would support the companys data objectives. Good answers connect specific technical skills and project outcomes to the companys announced or inferred data and engineering priorities.
Automated Reporting & Report Development
Build automated reports that refresh on schedule. Understand refresh schedules, data pipeline integration, and deployment to production. Create parameterized reports for different stakeholder needs. Know how to version control and manage report changes.
Analytical Data Systems and Warehousing
Architectures and operational patterns for analytical workloads and reporting. Coverage includes data warehouses, data marts, column oriented analytic storage, data lake and lakehouse architectures, extract transform load and extract load transform pipelines, batch and streaming ingestion, schema on read versus schema on write, materialized views and aggregation strategies, columnar compression and storage formats, partitioning and clustering tuned for analytic queries, cost versus performance trade offs for managed cloud services, and integration with business intelligence and reporting tools. Candidates should be able to distinguish online analytical processing from online transaction processing and choose appropriate architectures and tools for large scale analytics, including managed offerings and cost optimization strategies.