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).
SQL Fundamentals and Query Writing
Comprehensive query writing skills from basic to intermediate level. Topics include SELECT and WHERE, joining tables with inner and outer joins, grouping with GROUP BY and filtering groups with HAVING, common aggregation functions such as COUNT SUM AVG MIN and MAX, ORDER BY and DISTINCT, subqueries and common table expressions, basic window functions such as ROW_NUMBER and RANK, union operations, and principles of readable and maintainable query composition. Also covers basic query execution awareness and common performance pitfalls and how to write correct, efficient queries for combining and summarizing relational data.
SQL Scenarios
Advanced SQL query design and optimization scenarios, including complex joins, subqueries, window functions, common table expressions (CTEs), set operations, indexing strategies, explain plans, and performance considerations across relational databases.
Join Operations and Multi Table Queries
Comprehensive mastery of joining data across two or more tables in Structured Query Language. Candidates should understand and be able to use inner join, left join, right join, and full outer join semantics, including how each type affects row inclusion and null propagation. Be familiar with self joins, cross joins and anti join and semi join patterns for filtering. Know how to write correct multi table join conditions to avoid inadvertent Cartesian products, how to deduplicate and validate results by checking row counts and key uniqueness, and how to handle nulls and duplicate column names. Understand when to prefer joins versus subqueries or common table expressions for clarity or performance. Be able to read and interpret execution plans and explain how join order, join algorithms such as nested loop join, hash join, and merge join, and appropriate indexing affect performance. Recognize differences in join syntax and behavior across Structured Query Language dialects, including use of USING versus ON clauses and older comma separated join styles. Practice building queries that combine filtering, aggregation, grouping, and joins across three or more tables to express realistic business logic while keeping correctness and performance in mind.
Relational Databases and SQL
Focuses on relational database fundamentals and practical SQL skills. Candidates should be able to write and reason about SELECT queries, JOINs, aggregations, grouping, filtering, common table expressions, and window functions. They should understand schema design trade offs including normalization and denormalization, indexing strategies and index types, query performance considerations and basic optimization techniques, how to read an execution plan, and transaction semantics including isolation levels and ACID guarantees. Interviewers may test writing efficient queries, designing normalized schemas for given requirements, suggesting appropriate indexes, and explaining how to diagnose and improve slow queries.
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).
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.
Advanced Querying with Structured Query Language
Covers authoring correct, maintainable, and high quality Structured Query Language statements for analytical and transactional problems. Candidates should demonstrate writing Select Insert Update and Delete statements and using filtering grouping ordering and aggregation correctly. Emphasis is on complex query constructs and patterns such as multi table joins and join condition logic self joins for hierarchical data nested and correlated subqueries common table expressions including recursive common table expressions window functions such as row number rank dense rank lag and lead set operations like union and union all and techniques for calculating running totals moving averages cohort metrics and consecutive event detection. Candidates should be able to break down and refactor complex requirements into composable queries for readability and maintainability while reasoning about performance implications on large data sets. Senior expectations may include mentoring on best practices for query composition and understanding how schema and configuration choices influence query performance.