Database Engineering & Data Systems Topics
Database design patterns, optimization, scaling strategies, storage technologies, data warehousing, and operational database management. Covers database selection criteria, query optimization, replication strategies, distributed databases, backup and recovery, and performance tuning at database layer. Distinct from Systems Architecture (which addresses service-level distribution) and Data Science (which addresses analytical approaches).
Storage and Database Infrastructure
Storage concepts: SSDs vs. HDDs, RAID configurations, storage protocols. Database troubleshooting basics, replication concepts, backup and recovery strategies, understanding query performance and index behavior, and storage at scale.
Working with Sample Datasets and Schemas
Get comfortable quickly understanding an unfamiliar database schema before you ever write a query. Practice identifying primary and foreign keys, tracing relationships between tables (one-to-many, many-to-many, self-referencing), and distinguishing natural keys from surrogate keys. Learn a methodical exploration approach: skim table and column names, check information_schema or an ER diagram if one exists, follow foreign key chains outward from a core entity, and note nullable columns and naming conventions that hint at business rules. This skill transfers across domains, whether the schema is e-commerce (orders, customers, products), SaaS (accounts, users, subscriptions), or a revenue/CRM tech stack (leads, accounts, opportunities, interactions).
Database and Application Forensics
This topic assesses skills for recovering and interpreting evidence stored at the application and database layers. Candidates should understand database internals such as transaction logs, binary and write ahead logs, and techniques for restoring or carving deleted records. Evaluation includes parsing application specific storage formats, reconstructing user actions from API and audit logs, handling cloud hosted and distributed databases, and coordinating with engineering to retrieve durable artifacts. Candidates should also discuss challenges such as replication, eventual consistency, and legal constraints around retention and privacy.
Data Organization and Tracking
Designing, structuring, and maintaining data models and lightweight tracking systems that support operational work such as records, cases, vendors, projects, budgets, and compliance obligations. Candidates should be able to define the right fields and metadata, unique identifiers, relationships between entities, lifecycle statuses, milestone and deadline tracking, recurrence or renewal triggers, and reporting requirements. Discussion should include choices between normalized and pragmatic schemas, tagging and taxonomy, searchability and indexing, dashboards and metrics for stakeholders, integration considerations with adjacent line-of-business systems, data governance, ownership and stewardship, access controls and privacy, retention and audit trail policies, and practical implementation approaches from spreadsheets to databases and commercial platforms.
Database Fundamentals and Storage Engines
Core principles and components of data storage and persistence systems. This includes storage engine architectures and how they affect query processing and performance; transactions and isolation including atomicity, consistency, isolation, and durability; concurrency control and isolation levels; indexing strategies and how indexes affect read and write amplification; physical versus logical storage and object, block, and file storage characteristics; caching layers and cache invalidation patterns; replication basics and how replication affects durability and read performance; backup and recovery techniques including snapshots and point in time recovery; trade offs captured by consistency, availability, and partition tolerance reasoning; compression, cost versus performance trade offs, data retention, archival, and compliance concerns. Candidates should be able to reason about durability, persistence guarantees, operational recovery, and storage choices that affect latency, throughput, and cost.