Why Your IT Team Shouldn’t Take on DBA Roles (and How It Threatens Your Environment’s Performance)

Assigning Database Administration (DBA) responsibilities to generalist IT teams, whether they are Developers, Infrastructure Analysts, or DevOps Engineers, is not a cost optimization; it is the institutionalization of technical risk. This common practice creates an expertise vacuum in the most critical layer of the technology stack. The result is a silent accumulation of technical debt, which manifests as continuous performance degradation, systemic instability, and, ultimately, operational incidents that directly impact the business’s revenue and reputation.

The problem lies not in the competence of the IT team, but in a fundamental failure of role allocation. The mindset, tools, and objectives of a developer or an infrastructure engineer are fundamentally different from those of a specialist DBA. The attempt to merge these functions does not create a “multi-potential” professional; it dilutes expertise, masks risks, and ensures that the data layer, the stateful and most complex component of the environment, is managed reactively and superficially.

HTI Tecnologia, whose sole function is data reliability engineering, operates precisely in this blind spot. Our 24/7 support and consulting service aims not just to solve problems but to correct the structural failure that allows them to occur, replacing diluted responsibility with a team of dedicated specialists.

This article dissects the technical causes and consequences of overloading your IT team with DBA functions, demonstrating how this practice directly threatens the performance and resilience of your environment.

Why is the DBA Role Being Consolidated?

The dilution of DBA responsibility is rarely a deliberate strategic decision. It emerges from a combination of organizational, cultural, and financial factors that, although well-intentioned, are based on fundamentally flawed premises about the nature of data management.

The Cost Optimization Argument

The most common justification is the perception of savings. Hiring a dedicated senior DBA is seen as a significant personnel expense. The apparent alternative is to distribute their tasks among existing IT team members, a calculation that ignores the Total Cost of Ownership (TCO) of this decision. The real cost is not in the absence of a salary, but in the price of inefficiency, rework, and the incidents that this lack of expertise generates. A single hour of downtime caused by a poorly optimized query or a poorly configured disaster recovery plan can exceed the annual cost of a specialized service.

The “You Build It, You Run It” Paradigm and the Data Layer

The DevOps culture, with its mantra “You Build It, You Run It,” is a powerful accelerator for application development, especially in stateless microservices architectures. However, the dogmatic application of this principle to the stateful data layer is dangerous. A developer can, through automation, provision a database-as-a-service (DBaaS) instance in the cloud in minutes. This, however, does not qualify them to optimize its execution plans, design its replication topology for high availability, or plan a backup and recovery strategy that meets the business’s RPO/RTO objectives. The ability to “operate” the infrastructure is different from the ability to “manage” the data system.

Cloud Abstraction and the Illusion of Simplicity

The rise of managed database services (like Amazon RDS, Azure SQL Database) has created an illusion of simplicity. The complexity of operating system provisioning, patch application, and basic backup configuration has been abstracted away. However, the intrinsic complexity of the DBMS has not disappeared; it has just become less visible. Performance still depends on a correct indexing strategy. Security still requires a granular permission setup. Cost optimization (FinOps) still requires a deep workload analysis to correctly size the instance and the I/O. The cloud makes it easy to start, but it does not eliminate the need for expertise to operate at scale in an optimized and secure manner.

The Central Conflict:

The fundamental reason why the consolidation of roles fails is that the different IT disciplines are optimized for distinct outcomes. Forcing a team to operate outside its primary mindset in the data layer creates a technical conflict of interest that leads to suboptimal decisions.

The Developer’s Mindset: The primary focus is on delivery speed and functional correctness. The success metric is the lead time for changes. The goal is to deliver new features that meet the business requirements in the shortest possible time. Performance is a concern, but its validation often occurs in development environments with negligible data volumes, where an inefficient query might seem performant.
The Infrastructure/SRE Engineer’s Mindset: The focus is on the availability and reliability of the infrastructure as a whole. The success metrics are the “Nines” of uptime (99.99%), network latency, and host health (CPU, RAM, Disk). The database is seen as a process, a “workload” that needs to be online and responsive. Monitoring focuses on the “shell” of the system, not on its internal workings.
The Specialist DBA’s Mindset: The focus is on the long-term integrity, performance, and scalability of the data. The success metric is the efficiency of a query’s execution, measured in logical reads, CPU time, and the execution plan. The DBA analyzes the impact of a schema change on data integrity, how the concurrency control mechanism (locking vs. MVCC) will affect the workload, and whether the current architecture will support the business’s growth in the next 18 months.

When a developer acts as a DBA, they prioritize functionality, potentially introducing queries that do not scale. When an infrastructure engineer acts as a DBA, they ensure the server’s uptime but may not have the visibility to diagnose a memory latch contention or a bloat problem in a PostgreSQL table.

Technical Consequences

This divergence of mindsets is not an academic debate. It produces measurable and cumulative technical failures that silently degrade the health of the data environment.

Performance Degradation at the Query Level

This is the most common symptom and the source of most user-perceived slowness problems. Without the governance of a specialist, the application’s codebase becomes a minefield of inefficiencies.

Systematic Neglect of the Execution Plan: The most important diagnostic tool for a DBA, the EXPLAIN command, is systematically ignored. The development team validates the logical correctness of the query, not its execution efficiency. A query that uses a Nested Loop Join may work perfectly in a staging environment with a thousand records but becomes the cause of a performance incident in production when the tables reach millions of records, leading the DBMS to a massive consumption of I/O and CPU.
Proliferation of SQL Anti-Patterns: Queries that use SELECT * on wide tables, the use of functions in WHERE clauses that nullify the effectiveness of indexes, or the lack of explicit JOINs become the norm. Each of these anti-patterns adds a small overhead that, multiplied by thousands of executions per minute, results in a systemic degradation.
Reactive and Inefficient Indexing Strategy: Indexes are created without a cohesive strategy, usually as a reaction to a specific slowness problem. This leads to a scenario with redundant indexes, which add no read value but impose a performance penalty on all write operations (INSERT, UPDATE, DELETE), and missing indexes for other critical queries.

Fragility of the Architecture and Data Design

The absence of a data specialist leads to short-term architectural decisions that compromise long-term scalability, resilience, and integrity.

Accumulation of Schema Debt: Decisions about data types (e.g., using VARCHAR(255) to store a ZIP code), the lack of foreign key constraints to ensure referential integrity, and inadequate normalization are made for development convenience, without considering the future impact on performance and data quality.
Absence of Capacity Planning: The IT team reacts when the disk fills up or the database server’s CPU saturates. A specialist DBA, on the other hand, analyzes data and workload growth trends to predict future infrastructure needs. This proactive analysis allows for budgetary and technical planning, preventing the company from discovering that its infrastructure is undersized during a critical business event, like a Black Friday.
Misunderstood High-Availability Architectures: Setting up a replication or a cluster using a tutorial is trivial. Managing this topology in production is a highly complex discipline. A generalist team may not understand the nuances of replication lag and its causes (I/O on the replica, long transactions on the primary), the risks of split-brain in a cluster, or how to perform a safe and tested failover. High availability becomes a “black box” that works until the moment of disaster, when it is discovered that it was not resilient.

Superficiality of Diagnosis and Incident Response

A generalist IT team monitors what it knows: CPU, memory, disk, and network. This monitoring, although necessary, is dangerously superficial for a DBMS.

The “Wait Events” Blind Spot: The most important metric for diagnosing a database’s performance is the wait events. They indicate exactly what the database sessions are waiting for (I/O, locks, latches, network, etc.). A team without DBA expertise does not monitor or know how to interpret these events, remaining blind to the root cause of the slowness and focusing only on the symptoms (high CPU).
Incorrect Diagnosis and the Expensive Solution: Faced with a slowness problem, the instinctive reaction of an infrastructure team is to scale up the resources—a larger cloud instance, more CPU, more RAM. This is an expensive solution that, in most cases, only masks the real problem, which is an inefficient query or a poor indexing strategy. The cloud cost increases, but the root cause of the problem persists.
Untested Disaster Recovery Plans: A backup is configured and presumed to be functional. A specialist DBA knows that a backup that has never been tested for restoration is, for all intents and purposes, a hope, not a plan. They design and, crucially, execute periodic disaster recovery tests to ensure that the company’s RPO (Recovery Point Objective) and RTO (Recovery Time Objective) can be met in practice.

The Strategic Solution: Augment the Team, Don’t Overload It

The solution to this structural problem is not to blame the IT team. It is to recognize that database administration is a specialized discipline that requires focus and depth. The most effective approach in terms of cost and risk is to augment your generalist team with specialized expertise.

Partnering with HTI Tecnologia offers a model that directly solves the problems discussed.

Focus and Deep Technical Expertise: Our sole responsibility is the health, performance, and security of your data. Our Database Consulting team brings a depth of knowledge in multiple platforms (SQL Server, Oracle, PostgreSQL, MySQL, MongoDB, etc.) that is impossible to replicate in a generalist in-house team.
Unlocking the Potential of Your In-House Team: By taking responsibility for the data layer, your development and DevOps team can focus 100% on what they do best: building and delivering features that add value to the business. We become an accelerator, not a bottleneck, for your innovation pipeline.
Operational Continuity and Risk Reduction: Our 24/7 Support and Sustaining service eliminates the risk of a single point of failure and ensures that an expert will always be available to prevent or respond to incidents, ensuring your business continuity through a robust SLA.

Assign the Critical Role to the Specialist

Continuing to allow DBA responsibilities to be diluted within your IT team is not a strategy; it is a bet. It is betting that performance problems will not impact your customers, that a disaster recovery failure will never happen, and that your team can become experts in everything, simultaneously.

Data management is a highly specialized engineering function. Recognizing this and assigning this responsibility to a team with dedicated focus and depth, whether internal or outsourced, is the first step to building a truly robust, performant, and secure data infrastructure.

Is your IT team showing signs of being overloaded with DBA functions? Schedule a conversation with one of our specialists and discover how focused expertise can protect your environment and accelerate your business.

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