These skills do not affect the main grade but show the breadth of your profile.
AI Coding Assistants
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Familiar with AI assistants ChatGPT and Claude. Uses them for generating SQL queries, explaining execution plans, and finding solutions to common database problems.
Effectively applies LLMs for DBA tasks: generating optimized queries, analyzing execution plans, creating migration scripts, automating routine database maintenance operations.
Defines AI adoption strategy for the DBA team: standards for LLM-based SQL review, automating runbook generation, AI-assisted incident diagnostics.
Uses GitHub Copilot for writing SQL queries and DBA scripts: SELECT/JOIN autocompletion, CREATE TABLE generation. Verifies suggested code for data type correctness and constraints.
Applies Copilot for DBA tasks: generating stored procedures, triggers, migration scripts. Uses AI for writing monitoring queries (Performance Schema, pg_stat_statements). Critically evaluates suggestions.
Defines AI tooling standards for the DBA team: guidelines for code review of AI-generated SQL, restrictions for production scripts. Implements AI-assisted query optimization and anomaly detection in workflows.
Alerting & On-Call
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Understands basic database SLIs: query latency (p50, p99), availability (uptime), error rate. Monitors SLIs via dashboards. Participates in incident response when database service SLOs are violated.
Defines SLI/SLO for database services: query latency budgets, connection availability, replication lag thresholds. Configures SLO-based alerts: error budget burn rate, latency degradation. Participates in SLO review.
Defines SLO standards for the data platform: SLO templates by DB tier, escalation policies, SLO review cadence. Coordinates SLO agreements between DBA and product teams. Establishes database reliability targets.
Shapes organizational SLO strategy for the data tier: SLO framework covering all DBMSes, SLA for internal database-as-a-service, SLO-driven investment decisions. Defines reliability culture for database engineering.
Algorithms & Data Structures
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Understands the fundamentals of Algorithms & Complexity at a basic level. Applies simple concepts in work tasks using SQL/Python. Follows recommendations from senior developers when solving problems.
Independently applies algorithmic thinking in database work: evaluates query execution plans, understands index algorithm selection for different workloads, selects appropriate sorting strategies for large result sets. Analyzes computational complexity of stored procedures and complex queries.
Defines algorithmic efficiency standards for SQL queries and procedures. Reviews queries focusing on operation complexity: nested loops vs hash joins, cardinality estimation. Implements automated query plan analysis in CI.
Understands basic database data structures: table schemas, primary/foreign key relationships, basic index types. Follows team conventions for naming tables and columns, writing simple queries using proper join structures.
Independently selects appropriate data structures for database design: proper index types for query patterns, partitioning strategies for large tables, JSON vs relational structures for semi-structured data. Understands trade-offs between normalization levels for different workload types.
Defines data modeling standards: normalization vs denormalization for different workloads, table partitioning strategies. Reviews index structures, partitioning schemes, and storage strategies for OLTP and OLAP workloads.
Application Security
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Knows main OWASP Top 10 vulnerability categories, especially SQL injection. Understands basic database protection principles: parameterized queries, least privilege.
Applies OWASP principles for database protection: configuring row-level security, encrypting sensitive data, access auditing. Performs regular privilege analysis and removes excessive permissions.
Defines data security policy: data classification standards, access audit processes, security incident response, GDPR/PCI-DSS compliance at the DBMS level.
Knows basic secure coding principles: avoiding SQL injection, input validation. Applies parameterized queries and least privilege when working with databases.
Writes secure DBA scripts: parameterizing all queries, protecting against injection in dynamic SQL, secure credential storage, logging with sensitive data masking.
Defines secure development standards for the DBA team: mandatory security review of scripts, privilege management processes, stored procedure vulnerability auditing.
Caching
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Understands caching principles and Redis purpose. Knows how cache interacts with the database: cache-aside, write-through, TTL. Understands the impact of cache on database load.
Configures Redis as a cache for database offloading: defining caching strategies (cache-aside/write-behind), configuring TTL, monitoring hit rate. Ensures cache-database consistency.
Defines caching architecture for the data platform: cache-invalidation standards, cache layer monitoring and alerting, warm-up strategies after restart, Redis capacity planning.
Clean Code & Refactoring
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Understands basic code quality principles for database scripts and stored procedures. Follows team naming conventions for tables, columns, and SQL formatting standards. Writes simple, clean migration scripts and basic stored procedures. Accepts code review feedback on query structure and naming consistency.
Independently applies code quality practices in database development. Writes well-structured stored procedures and migration scripts with proper naming and documentation. Understands trade-offs between query optimization and code maintainability. Reviews database code for index usage, transaction isolation, and naming consistency.
Establishes SQL and DBA script quality standards: SQL linting (sqlfluff), code review for migrations, unit tests for stored procedures. Implements automated review for DDL changes and backward compatibility checks.
Cloud Providers
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Works with AWS database services: RDS for managed databases, S3 for backup storage, CloudWatch for basic monitoring. Understands IAM roles for database access, VPC and security groups for network isolation.
Administers AWS database services: RDS Multi-AZ, Aurora replicas, ElastiCache. Configures Parameter Groups, automated snapshots, Performance Insights. Optimizes costs via Reserved Instances for the database tier.
Defines AWS database strategy: choosing between RDS, Aurora, self-managed EC2 by workload. Establishes cost management for the database tier, backup policies. Coordinates cloud database operations between DBA and DevOps teams.
Code Review
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Participates in SQL code and migration reviews: checks syntax, data types, naming conventions. Learns to find potential issues in schemas. Asks questions and follows feedback from senior DBA.
Conducts database change reviews: evaluates migrations for backward compatibility, checks index strategy, analyzes query performance impact. Verifies rollback plans for each DDL change.
Defines database code review standards: mandatory checklists for DDL/DML changes, automated checks (backward compat, performance regression), approval workflows for production schema changes.
Concurrency & Parallelism
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Understands asynchronous execution concepts: callbacks, promises, event loop. Uses async calls when writing monitoring scripts and DBA task automation.
Applies asynchronous programming for DBA tasks: parallel monitoring of multiple DB instances, non-blocking backup operations, concurrent maintenance script execution.
Defines async processing patterns for the DBA platform: strategies for parallel cluster maintenance, distributed operation coordination, timeout and retry handling for long-running tasks.
Understands multithreading concepts: threads, locks, race conditions. Knows how concurrent database access creates problems: deadlocks, contention, transaction isolation levels.
Diagnoses and resolves concurrent database access issues: deadlock analysis, transaction isolation level configuration, lock contention optimization. Configures connection pooling.
Defines concurrent data access standards: isolation level recommendations for different workloads, deadlock resolution policies, architectural patterns for high-concurrency.
Containerization
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Runs DBMS in Docker for local development: MySQL, PostgreSQL, ClickHouse containers. Understands docker-compose for multi-container stacks (DB + monitoring). Maps volumes for persistent data storage.
Manages containerized databases: configuring resource limits (memory, CPU), persistent volumes, custom configurations via bind mounts. Creates Docker images for DBA tools (backup scripts, monitoring agents).
Defines container standards for the database tier: base images for DBA tooling, security scanning policy, resource allocation guidelines. Coordinates Docker usage for dev/staging DB environments and DBA automation.
Data Modeling
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Designs simple schemas: normalization up to 3NF, data type selection, constraints (NOT NULL, UNIQUE, FK). Creates ER diagrams for small modules. Understands the difference between OLTP and OLAP modeling.
Designs production schemas: choosing between normalization and denormalization by workload, partitioning strategy, data archiving. Considers growth patterns when selecting data types and partition keys.
Defines data modeling standards: naming conventions, data types per DBMS, partitioning guidelines. Conducts design reviews for critical schemas. Coordinates data modeling practices between product and DBA teams.
Shapes organizational data modeling strategy: domain-driven data design, data mesh principles, enterprise data model. Defines modeling standards for different DBMSes and use cases, including multi-model approaches.
Database Optimization
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Creates basic indexes: B-tree for WHERE and JOIN, understands the difference between clustered and non-clustered. Uses EXPLAIN to detect missing indexes. Knows about index scan vs table scan.
Designs efficient indexes: composite indexes with correct column order, covering indexes to avoid lookups. Analyzes index usage statistics, finds unused and duplicate indexes for optimization.
Defines indexing standards: guidelines for index types by workload, automated index recommendations (pg_qualstats, sys.dm_db_index_usage_stats). Implements periodic index audits in operational processes.
Git & Workflows
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Uses Git for versioning database artifacts: migration scripts, stored procedures, configurations. Understands branch/merge workflow. Commits schema changes with descriptive messages.
Manages database code in Git: branching strategy for migration scripts, merge conflict resolution in SQL files. Configures Git hooks for SQL linting. Organizes repository structure for database projects.
Defines Git standards for database artifacts: repository structure, branching policy, commit conventions for DDL changes. Coordinates version control practices between DBA and development teams.
Infrastructure as Code
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Uses Terraform for provisioning DB infrastructure: RDS instances, parameter groups, subnet groups. Understands plan/apply workflow. Reads existing modules for database resources. Follows team IaC practices.
Writes Terraform modules for database provisioning: RDS/Aurora clusters, replicas, automated backups. Manages state for database resources. Configures parameter groups and security groups via IaC.
Defines IaC standards for the database tier: module registry for DB resources, change management for stateful infrastructure. Implements policy-as-code for database security (encryption, network isolation). Coordinates Terraform adoption for DBA.
Integration Testing
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Understands the role of integration testing when working with databases. Writes basic tests to verify correct application-database interaction after migrations.
Develops integration tests for DBA operations: testing migrations on production data copies, failover scenario testing, replication and data consistency validation.
Defines integration testing standards for the DBA team: mandatory scenarios before DBMS upgrades, application compatibility testing, pre-production validation processes.
Kubernetes & Orchestration
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Understands basic Kubernetes concepts for running database workloads in containers. Follows team guidelines for connecting to database pods, checking persistent volume claims, and viewing database operator logs. Uses existing configurations for StatefulSet-based database deployments.
Manages databases in Kubernetes: configuring StatefulSets with persistent storage, anti-affinity for replica distribution, Helm charts for DBMS cluster deployment.
Defines database-on-Kubernetes strategy: criteria for K8s vs bare-metal, operator standards, zero-downtime DBMS upgrade processes, disaster recovery in containerized environments.
Logging
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Understands structured logging for database operations: JSON format for DBA scripts, mandatory fields (timestamp, db_name, operation). Uses structured logs for troubleshooting database issues.
Implements structured logging in DBA tools: unified format for backup/restore scripts, migration logs, maintenance operations. Configures correlation IDs for tracking database operations through the pipeline.
Defines logging standards for all database operations: mandatory fields, severity classification, PII masking for query parameters. Coordinates logging practices between DBA and application teams.
Metrics & Monitoring
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Uses Grafana for DB monitoring: reading dashboards with metrics (connections, QPS, replication lag). Understands basic MySQL/PostgreSQL exporter metrics. Sets up simple threshold-based alerts.
Configures Prometheus monitoring for databases: mysqld_exporter, postgres_exporter, custom queries for business metrics. Creates Grafana dashboards: query performance, buffer pool hit ratio, lock waits. Configures alerting rules.
Defines database monitoring standards: mandatory DBMS metrics, dashboard templates, alerting severity levels. Coordinates monitoring between DBA and SRE. Implements monitoring as part of database provisioning.
Networking
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Understands basic networking concepts: TCP/IP, DNS, ports. Knows networking aspects of database work: connection strings, DBMS ports, basic network connectivity diagnostics.
Applies networking knowledge to DBA tasks: configuring pg_hba.conf and firewall rules, diagnosing connection latency, optimizing TCP keep-alive for stable DB connections.
Defines network architecture for the data platform: network segmentation standards, VPN/PrivateLink for cross-region replication, access policies and network anomaly monitoring.
OOP & Design Patterns
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Knows fundamental design patterns: Singleton, Observer, Factory. Understands how patterns apply when creating DBA tools and database automation scripts.
Applies design patterns in DBA tools: Repository for DBMS metadata access, Strategy for different backup strategies, Observer for database event monitoring.
Defines a pattern catalog for the DBA platform: standard solutions for monitoring, failover, backup/restore. Mentors the team on applying patterns in database administration context.
Knows core OOP principles: encapsulation, inheritance, polymorphism. Applies basic patterns when writing automation scripts for database management.
Applies OOP when developing DBA tools: connection wrapper classes, abstractions for different DBMSes (PostgreSQL/MySQL/Oracle), patterns for migration scripts.
Defines architectural standards for DBA tooling: DBMS abstraction patterns, extensibility principles for new storage systems, code review criteria for automation scripts.
Profiling
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Monitors database CPU utilization: identifies high-CPU queries via slow query log and top/htop. Understands the relationship between CPU and query complexity. Uses SHOW PROCESSLIST to find active heavy queries.
Profiles database CPU usage: pt-query-digest for MySQL, pg_stat_statements for PostgreSQL, wait events analysis. Identifies CPU-bound queries and optimizes them. Configures CPU-related alerts.
Defines CPU performance standards: baseline metrics, alert thresholds, capacity planning. Coordinates CPU optimization between DBA and infrastructure teams. Implements performance testing in the database change pipeline.
Shapes performance engineering strategy for the data platform: automated performance analysis, hardware selection criteria (CPU architecture for database workloads), performance budgets. Defines investments in database performance tooling.
Prompt Engineering
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Understands basic principles of working with AI models. Formulates prompts to LLMs for help with SQL queries, finding database error causes, and generating documentation.
Applies prompt engineering for DBA tasks: structured SQL generation, execution plan analysis via AI, creating runbooks for typical incidents with specific DBMS context.
Defines AI strategy for the DBA team: standard prompts for routine tasks, knowledge bases for contextual queries, AI integration into incident response and capacity planning processes.
Relational Databases
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Works with PostgreSQL: creating tables with constraints, basic queries, psql CLI. Understands data types (JSONB, arrays), sequences, schemas. Can use EXPLAIN ANALYZE for simple query analysis.
Administers PostgreSQL: configuring shared_buffers, work_mem, effective_cache_size. Works with pg_stat_statements for load analysis, configures PgBouncer for connection pooling. Performs VACUUM and REINDEX operations.
Defines PostgreSQL standards: configuration templates by tier, monitoring checklists, backup/recovery SLA. Conducts capacity planning, plans major version upgrades. Coordinates PostgreSQL best practices across teams.
REST API
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Knows basic REST API principles: resources, HTTP methods, response codes. Understands how applications interact with the database through APIs and what access patterns this creates.
Designs REST APIs for DBA administrative tasks: endpoints for DB health monitoring, backup management, query statistics viewing. Considers security and authentication.
Defines API standards for DBA team tools: unified contracts for monitoring, configuration management, auditing. Designs an API gateway for unified access to different DBMSes.
System Design
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Understands basic architectural concepts of Disaster Recovery Design. Follows team's architectural decisions. Understands main patterns.
Participates in DR processes: performs backup verification, tests restore procedures per runbooks. Configures automated snapshots and cross-region backup copying. Monitors replication lag and backup status.
Defines DR standards for the data platform: RPO/RTO by tier, DR testing schedule, failover procedures. Coordinates DR drills with cross-functional teams. Creates incident playbooks for database failures.
Shapes organizational disaster recovery strategy: multi-region active-active vs active-passive, cross-cloud DR, RTO/RPO frameworks. Defines DR governance, compliance requirements, and investments in database resilience.
Understands basic system design concepts. Knows the database's role in application architecture: access patterns, load characteristics, consistency requirements.
Applies system design principles when planning DB infrastructure: choosing DBMS type, defining availability requirements, designing replication and backup schemes.
Defines data architecture for products: polyglot storage (SQL + NoSQL + Cache), scaling strategies, multi-region architecture for global applications with low latency.
Unit Testing
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Understands unit testing principles and its role in quality assurance. Writes basic tests for migration scripts and stored procedures, verifying data transformation correctness.
Develops unit tests for DBA scripts: testing migrations on a test DB, validating stored procedures with mock data, verifying ETL transformation and trigger correctness.
Defines testing strategy for the DBA team: coverage standards for migrations, mandatory smoke tests after deployment, test integration into the DB CI/CD pipeline.