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).
Geospatial and Time-Series Data
Specialized data shapes and their stores: geospatial data types, spatial indexing, and location queries; and time-series data with high-ingest, retention, downsampling, and cardinality management. Covers storage-engine tuning such as compression, chunking, and indexing for range queries, and when purpose-built extensions or databases beat general-purpose stores for these workloads. A niche but recurring topic for location- and telemetry-heavy systems.
SQL Dialects and Built-in Functions
Portability and idiom differences across SQL engines (PostgreSQL, Redshift, MySQL, SQL Server, and others), plus fluent use of built-in string, date/time, and type-conversion functions. Covers dialect-specific syntax, function availability, and writing queries that behave predictably across platforms. Relevant when candidates move between database ecosystems.
SQL Joins and Set Operations
Combining data across multiple tables using inner, outer, cross, and self joins, plus set operations (UNION, INTERSECT, EXCEPT). Covers join-key selection, fan-out and row-multiplication pitfalls, merge strategies, and integrating data from disparate sources. A high-frequency interview surface for anyone who queries relational data.
Data Modeling and Schema Design
Designing relational schemas end to end: entity-relationship modeling, normal forms and deliberate denormalization, primary/foreign keys, data types, and integrity constraints, together with applied schema design driven by real business requirements and query access patterns. Covers modeling a domain from ambiguous requirements, choosing structures that serve the queries a system must run, trading normalization for correctness against denormalization for read performance, and evolving schemas as needs change. Foundational data-modeling judgment for building and reviewing databases, tested through open-ended domain-modeling prompts.
Consistency Models and Distributed Databases
Data correctness across distributed systems: strong versus eventual consistency, the CAP and PACELC trade-offs, consensus and quorum reads/writes, and consistency-versus-availability decisions. Covers how distributed databases reconcile replicas and what guarantees applications can rely on. A staple of distributed-systems and architecture interviews.
SQL Query Fundamentals
Core SQL for reading and shaping data: SELECT, filtering with WHERE, sorting, DISTINCT, and single-table aggregation with GROUP BY, HAVING, and aggregate functions. Covers reasoning about NULL handling, grouping semantics, and writing correct queries against a given schema. The baseline query-writing surface most data and engineering interviews open with.
Query Optimization and Execution Plans
Making queries fast: reading and interpreting execution/explain plans, identifying full scans, spotting SQL anti-patterns, and rewriting queries for better performance. Covers how the planner chooses join order and access methods, and how statistics drive those choices. A core skill for anyone responsible for query performance in production.
Advanced SQL: Window Functions, CTEs, and Subqueries
Analytical SQL for complex problems: window functions (ranking, running totals, LAG/LEAD, partitioned aggregates), common table expressions including recursive CTEs, and scalar, nested, and correlated subqueries. Covers when each construct is the right tool and how they compose for multi-step analysis. The differentiator between basic and senior SQL competence.
NoSQL and Polyglot Persistence
Non-relational data stores and mixing them: key-value, document, wide-column, and graph databases, their data models and access patterns, and polyglot persistence where different stores serve different needs. Covers when NoSQL beats relational and how to integrate multiple stores coherently. Increasingly common in system-design and data-platform interviews.