Master Your Database Developer Interview
Explore real questions, model answers, and actionable tips to showcase your expertise.
- Real‑world technical and behavioral questions
- STAR‑structured model answers
- Competency‑based evaluation criteria
- Practice pack with timed rounds
Technical - SQL
In a reporting project I needed to combine customer data with order data.
Show all customers and their orders, but also list customers without orders.
Used INNER JOIN to retrieve only customers with matching orders for the sales summary, and LEFT JOIN when generating a complete customer list that included those without orders, ensuring null order fields were handled appropriately.
The sales summary was accurate, and the full customer list highlighted 15 inactive customers, enabling targeted outreach.
- How does the query planner treat these joins?
- What indexes would you create to optimise each join?
- Correct definition of each join type
- Clear use‑case illustration
- Understanding of performance impact
- Confusing LEFT with RIGHT JOIN
- Vague answer without concrete example
- INNER JOIN returns rows with matching keys in both tables
- LEFT JOIN returns all rows from the left table plus matching rows from the right, filling non‑matches with NULL
- Use INNER JOIN for reports where only related records matter
- Use LEFT JOIN when you need a full set from the primary table regardless of matches
During data cleanup I discovered repeated employee IDs in the Employees table.
Identify all employee IDs that appear more than once.
Wrote a GROUP BY query on the employee_id column with HAVING COUNT(*) > 1 to list duplicate IDs, then joined back to the original table to see full duplicate rows.
Detected 8 duplicate employee records, which were merged, improving data integrity.
- How would you delete the duplicates while keeping one record?
- Can you achieve the same result using a window function?
- Correct use of GROUP BY and HAVING
- Clarity of steps
- Suggesting DISTINCT as a solution
- SELECT column, COUNT(*) FROM table GROUP BY column HAVING COUNT(*) > 1;
- Optionally join this result back to the original table to retrieve full rows
Technical - Database Design
Designing a new inventory system for a retailer.
Ensure the schema eliminates redundancy and update anomalies.
First applied 1NF by removing repeating groups, then 2NF by moving partial dependencies to separate tables, and finally 3NF by eliminating transitive dependencies, creating distinct tables for Products, Categories, and Suppliers with proper foreign keys.
The final schema reduced data duplication by 40% and simplified maintenance, leading to faster query performance.
- What trade‑offs might you consider when denormalizing for performance?
- How would you handle many‑to‑many relationships?
- Accurate definition of each normal form
- Logical progression of steps
- Realistic example
- Skipping 2NF or 3NF explanations
- 1NF – atomic columns, no repeating groups
- 2NF – all non‑key attributes fully depend on the whole primary key
- 3NF – remove transitive dependencies; non‑key attributes depend only on the primary key
Working on a real‑time analytics platform handling high‑velocity sensor data.
Select a storage solution that scales horizontally and supports flexible schemas.
Evaluated requirements and chose a document‑oriented NoSQL store because it allowed rapid ingestion of semi‑structured JSON payloads, offered automatic sharding, and provided low‑latency reads without complex joins.
The system handled a 5× increase in data volume with sub‑second query response, meeting SLA targets.
- How would you ensure data consistency in a NoSQL system?
- What challenges arise when migrating from NoSQL to relational?
- Clear justification based on scalability, schema flexibility, query patterns
- Awareness of trade‑offs
- Claiming NoSQL is always faster without context
- High write throughput and horizontal scalability needs
- Schema flexibility for evolving data structures
- Lack of complex relational joins
- Specific use‑cases: caching, session storage, real‑time analytics
Behavioral
Our e‑commerce site experienced a 30% slowdown during peak checkout hours.
Find and fix the root cause of the latency in the order processing database.
Analyzed execution plans, discovered missing indexes on the orders table, and identified a costly full‑table scan in a stored procedure. Implemented covering indexes, rewrote the procedure to use set‑based logic, and introduced query caching for static lookup tables.
Reduced checkout transaction time from 4.2 seconds to 1.1 seconds, eliminating cart abandonment spikes and improving revenue by ~5% during peak periods.
- What monitoring tools do you rely on for ongoing performance health?
- How do you balance index creation with write overhead?
- Systematic troubleshooting approach
- Technical depth in indexing and query optimization
- Quantifiable results
- Blaming hardware without investigating query design
- Used monitoring tools (e.g., DMVs, AWR) to pinpoint slow queries
- Identified missing indexes and inefficient query patterns
- Applied indexing and query refactoring
- Validated improvement with before/after metrics
While developing a healthcare application, we needed to meet HIPAA requirements for patient data.
Implement security controls that protect PHI at rest and in transit while ensuring auditability.
Enabled Transparent Data Encryption (TDE) for all databases, enforced column‑level encryption for SSNs, configured role‑based access controls with least‑privilege principles, and set up automated audit logging with tamper‑evident storage. Conducted regular vulnerability scans and quarterly compliance reviews.
The solution passed external HIPAA audit with zero findings, and no security incidents were reported over 18 months of production use.
- How do you handle key management for encrypted columns?
- What steps would you take after a suspected data breach?
- Understanding of encryption, access control, and audit mechanisms
- Alignment with regulatory standards
- Vague mention of ‘security’ without concrete controls
- Encryption at rest (TDE) and in transit (TLS)
- Column‑level encryption for sensitive fields
- RBAC and least‑privilege access
- Comprehensive auditing and monitoring
- Regular compliance testing
- SQL
- Stored Procedures
- Performance Tuning
- Data Modeling
- ETL
- Database Security
- Normalization
- Indexing
- NoSQL
