What a Lead } should know

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.

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.

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.

Defines data strategy at the product level. Establishes Apache Cassandra standards. Conducts data schema and scaling strategy reviews.

Defines organization-wide database backup and disaster recovery strategy aligned with business continuity objectives. Establishes backup standards, compliance frameworks, and recovery SLAs. Reviews and approves DR architectures across teams. Drives adoption of modern backup technologies.

Defines ClickHouse standards: naming conventions, partitioning strategy, retention policies. Coordinates ClickHouse usage for centralized storage of database metrics, audit logs, query analytics.

Defines data strategy at the product level. Establishes CockroachDB standards. Conducts data schema and scaling strategy reviews.

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.

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.

Defines migration standards: review process for DDL changes, automated backward compatibility check, approval workflow. Coordinates large migrations (sharding, engine changes) across teams with minimal impact.

Defines data strategy at the product level. Establishes DynamoDB standards. Conducts data schema and scaling strategy reviews.

Defines MongoDB data strategy at the product level including storage architecture and migration planning. Establishes database standards for schema design, indexing policies, and operational procedures. Conducts data architecture reviews and defines scaling strategies for multi-terabyte deployments across environments.

Defines organizational MySQL standards: versioning, configuration baselines, upgrade procedures. Coordinates zero-downtime major version migrations. Establishes MySQL infrastructure SLA and DR plans.

Defines data strategy at the product level. Establishes Neo4j standards. Conducts data schema and scaling strategy reviews.

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.

Defines query performance standards: response time SLA by tier, automated query review in CI, performance budgets. Coordinates optimization between DBA and development teams. Implements query governance.

Defines data strategy at the product level. Establishes Replication and High Availability standards. Conducts data schema and scaling strategy reviews.

Defines organization-wide transaction management standards and concurrency control policies across database platforms. Establishes guidelines for isolation level selection, distributed transaction patterns, and performance budgets for transaction processing. Conducts reviews of critical transaction designs ensuring data integrity at scale.

Defines data strategy at the product level. Establishes Vitess standards. Conducts data schema and scaling strategy reviews.

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.

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.

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.

Defines secrets management standards for the data platform: Vault policies for different database environments, rotation schedules, access review processes. Coordinates Vault integration with the database provisioning pipeline.

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.

Defines capacity planning processes: regular capacity reviews, automated threshold alerting, budget planning for database infrastructure. Coordinates capacity decisions with product and finance teams.

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.

Defines custom metrics standards for the database tier: business-level metrics (orders/sec, active users), database-specific (replication slot lag, vacuum progress). Coordinates custom metrics implementation between DBA and dev teams.

Defines database logging standards: mandatory fields, retention policies, log levels. Coordinates ELK integration with the database monitoring stack. Implements log-based alerting for critical database events.

Defines on-call standards for the database tier: rotation schedule, coverage requirements, alert fatigue reduction. Coordinates cross-team incident response. Conducts on-call retrospectives and improves processes.

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.

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.

Defines logging standards for all database operations: mandatory fields, severity classification, PII masking for query parameters. Coordinates logging practices between DBA and application teams.

Defines database code review standards: mandatory checklists for DDL/DML changes, automated checks (backward compat, performance regression), approval workflows for production schema changes.

Defines Git standards for database artifacts: repository structure, branching policy, commit conventions for DDL changes. Coordinates version control practices between DBA and development teams.

Defines database documentation standards: templates for database architecture docs, runbooks, data dictionaries. Coordinates documentation-as-code approach for database artifacts. Conducts documentation reviews.

Defines runbook standards: mandatory sections, testing requirements, regular review cadence. Coordinates runbook creation for new database services. Implements runbook automation via ChatOps and incident management tools.

Defines organization-wide database benchmarking strategy and performance standards. Establishes benchmark-driven decision-making processes for database technology selection and scaling planning. Conducts reviews of benchmark methodology ensuring statistical rigor and production relevance across teams.

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.

Defines Database Performance Tuning strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines I/O profiling strategy and storage performance standards across database teams. Establishes dashboards tracking IOPS saturation, latency percentiles, and write amplification. Conducts reviews of storage tier selection and backup I/O impact. Drives I/O-aware query scheduling practices.