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Database administrators

Also called DBA, SQL DBA, SQL server DBA, Oracle DBA, PostgreSQL DBA

In brief

In May 2025, BLS counted 69,990 US database administrators, with median pay of $104,620 a year. The earliest figure here is an estimate of 20,000 from 1980, not measured the same way as today's count. The last full technology era for database administrators: Cloud managed databases and NoSQL, 2009 to 2018.

What the sources say

Official outlook

BLS projects little or no change in employment from 2025 to 2035 (-0.1%).

BLS Employment Projections 2025-35

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Database administrators: jobs and pay since 1980 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/15-1242

Chicago

Future History. "Database administrators: jobs and pay since 1980." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/15-1242.

BibTeX

@misc{futurehistory_15_1242, author = {{Future History}}, title = {Database administrators: jobs and pay since 1980}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/15-1242}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
19752000now
2026
Known then as Data Processing Specialist / EDP Systems Analyst (pre-DBMS era)
Known then as Database Administrator / Data Base Administrator (CODASYL and IMS mainframe era)
Known then as Database Administrator / SQL DBA (relational database commercial era)
Known then as Database Administrator / Oracle DBA / SQL Server DBA (internet and enterprise tier)
Known then as Database Administrators and Architects (BLS SOC 15-1141 / 15-1245, combined code)
Known then as Database Administrators (BLS SOC 15-1242, split from Architects in 2018 SOC)
Known today as Database Administrators (BLS SOC 15-1242, split from Architects in 2018 SOC)
Projected to still be called Database Administrators (BLS SOC 15-1242, split from Architects in 2018 SOC)
Data from the May 2025 survey
US Employment
70K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$104,620
Est.

≈ $101,938 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1963

Charles Bachman at General Electric's information systems group completes the Integrated Data Store (IDS), widely recognized as the first general-purpose database management system. IDS is deployed to coordinate parts inventory, purchase orders, and production schedules at GE's manufacturing operations. There is no "database administrator" job title yet; the people who manage IDS are called data processing specialists. Bachman will receive the 1973 ACM Turing Award for this work, giving his acceptance lecture the title "The Programmer as Navigator" -- a description of how navigational databases required physical knowledge of pointer chains rather than logical queries.

Beat · 1970

Edgar F. Codd, an Oxford-educated mathematician working at IBM's San Jose Research Laboratory, publishes "A Relational Model of Data for Large Shared Data Banks" in the Communications of the ACM (June 1970). The paper proposes organizing data into tables with fixed-length records linked by values rather than physical pointers -- a model that could allow users to query data without knowing anything about its physical storage. IBM's management is initially slow to act on Codd's idea: the company's IMS database, already deployed for Apollo program data management, generates substantial revenue and IBM is reluctant to cannibalize it. This hesitation will allow Oracle to beat IBM to the commercial relational market by four years.

Beat · 1979

Relational Software Inc. -- later renamed Oracle Systems Corporation in 1983 -- ships Oracle version 2 (marketed as v2 from launch, skipping v1 for credibility), the first commercial relational database management system using SQL. Founded in 1977 by Larry Ellison, Bob Miner, and Ed Oates, the company is inspired directly by Codd's relational model paper and by IBM's System R project. Oracle v2 runs on the DEC PDP-11. It immediately creates demand for a new kind of database specialist: someone who can write SQL, design relational schemas, tune the cost-based optimizer, and manage tablespace allocation. The Oracle DBA as a distinct professional identity begins here.

Beat · 1983

IBM ships DB2 Version 1 for the MVS mainframe (generally available 1985), four years after Oracle's commercial launch. DB2 is announced as the strategic relational database platform for IBM's enterprise customer base. Named "DB2" to distinguish it from the earlier non-relational IMS (which IBM renamed DL/I), DB2 will eventually reach $1 billion in revenue by 1989 -- matching IMS's revenue -- and its DBA ecosystem will spawn a generation of IBM Certified Database Administrators and consulting firms specializing in DB2 tuning, migration, and optimization.

Beat · 1995

MySQL debuts on May 23, 1995, created by Swedish-Finnish developers Michael Widenius, David Axmark, and Allan Larsson as a lightweight, open-source database optimized for web read-heavy workloads. Released under a GPL license in 2000, MySQL becomes the engine of the LAMP stack -- Linux, Apache, MySQL, PHP -- that powers the early internet economy. Wikipedia, Facebook, YouTube, Twitter, and Flickr all run on MySQL at critical points in their growth. MySQL's ease of deployment allows web developers to spin up databases without a professional DBA, partially democratizing the field and creating the first large-scale environment where DBA expertise is optional rather than mandatory for routine database operations.

Beat · 2009

AWS launches Amazon RDS (Relational Database Service) in October 2009, followed by MongoDB 1.0 in November 2009. These two products arrive within weeks of each other and together mark the beginning of the cloud-database era. RDS handles provisioning, patching, backups, and multi-AZ failover without any DBA involvement in those tasks. MongoDB stores data as flexible documents rather than rigid tables, explicitly designed for developer velocity over DBA governance. Both products reduce the total labor required to run a database, and both shift some of the database management responsibility toward application developers and cloud providers. Oracle DBA consultant billing rates begin to face structural pressure.

Beat · 2018

Oracle launches the Autonomous Database in 2018 and at a conference Oracle CEO Mark Hurd states that if all databases moved to the autonomous platform, "the number of DBAs would go to zero." The quote receives wide coverage and wide pushback from the DBA community. Computer Weekly's coverage frames the question as existential. By October 2025, Oracle rebrands the product as AI Database 26ai and reports 66% DBA efficiency gains -- not zero DBAs, but meaningfully fewer routine-ops hours per terabyte managed. The statement and the subsequent reality illustrate the pattern that has characterized DBA employment across six decades: each automation wave was predicted to eliminate the role, each wave instead elevated the remaining human judgment requirements and reduced total headcount more slowly than predicted.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace database administrators? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
-0.1%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 15-1242 (Database administrators): 75.0 thousand jobs in 2025, projected 74.9 thousand in 2035, a change of -0.1 thousand (-0.1%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.8% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
Today, in this role

What's changing day to day for database administrators

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereManage autonomous and cloud-hosted database services (Oracle Autonomous Database, AWS Aurora, Azure SQL, Google AlloyDB), governing AI-driven auto-tuning and auto-scaling decisions, validating that autonomous changes align with application SLAs, and managing cost implications of automated resource scaling events.

Manage autonomous and cloud-hosted database services (Oracle Autonomous Database, AWS Aurora, Azure SQL, Google AlloyDB), governing AI-driven auto-tuning and auto-scaling decisions, validating that autonomous changes align with application SLAs, and managing cost implications of automated resource scaling events.[3],[9],[16]

Where your edge is

Develop cloud database governance skills: autonomous databases handle the maintenance loop, but DBAs remain essential for evaluating which workloads belong in which cloud database tier, validating that auto-scaling events do not create unexpected cost spikes, and ensuring that autonomous tuning changes do not violate application-layer assumptions about index availability or query plan stability.

AI is sitting alongside you hereMonitor database performance and detect query regressions using AI-powered observability platforms, reviewing AI-generated anomaly alerts (e.g

Monitor database performance and detect query regressions using AI-powered observability platforms, reviewing AI-generated anomaly alerts (e.g. a query baseline shift from 100 ms to 700 ms), validating root-cause diagnoses, and deciding whether to act on autonomous index or plan recommendations or override them with judgment about downstream application impacts.[7],[17],[18]

Where your edge is

Develop expertise in interpreting AI-generated query plans and execution traces rather than reading raw EXPLAIN output alone: learn to validate whether an AI-recommended index actually reduces write amplification for your workload, and build the institutional knowledge of which queries are regression-sensitive due to business criticality vs. background jobs.

AI is sitting alongside you hereOptimize query performance and indexing strategy: using AI indexing engines to run "What If?" simulations across hundreds of index combinations, then evaluating AI recommendations against write-load, storage, and application-contract constraints before applying changes to production.

Optimize query performance and indexing strategy: using AI indexing engines to run "What If?" simulations across hundreds of index combinations, then evaluating AI recommendations against write-load, storage, and application-contract constraints before applying changes to production.[8],[19],[13]

Where your edge is

Master the reasoning behind AI index recommendations rather than approving them wholesale: understand write amplification trade-offs for high-write tables, composite vs. partial index suitability, and when an AI-suggested index would hurt concurrent DML throughput more than it helps SELECT latency, the AI sees query patterns but not your SLAs.

Where database administrators go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

Senior DBAs who accumulate production-change authority, vendor relationship management, budget ownership, and the ability to communicate data infrastructure risk to non-technical stakeholders are well-positioned for IT management. The CRI gain reflects a materially more resilient role: IS Managers own the decision layer rather than the execution layer. The 2026 market specifically demands IT managers who can govern autonomous database adoption (Oracle Autonomous DB, AWS Aurora), set data governance strategy, and translate infrastructure investments into business outcomes: all skills that emerge naturally from a senior DBA background with cross-team authority.

What you'd add
· IT governance frameworks: ITIL v4, data governance frameworks (DAMA-DMBOK), and change advisory board (CAB) process management
· Budget management: CapEx/OpEx planning for database licensing (Oracle, SQL Server EAs), cloud database cost governance, vendor contract negotiation
· Data strategy communication: translating database SLA metrics, CRI risk scores, and infrastructure investment proposals into executive-level business-impact language
· Team leadership: managing hybrid teams of DBAs, data engineers, and cloud infrastructure engineers; workforce planning as autonomous database tools reduce headcount needs
· Certifications: ITIL 4 Managing Professional, PMP, or AWS/Azure cloud management credentials
What it takesA real upskill, but a natural one
Tools of the era

How has the work of database administrators changed since 1963?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Navigational DBMS: IDS (GE, 1963) and IMS (IBM/Rockwell, 1966)

    Charles Bachman's Integrated Data Store at General Electric and IBM's Information Management System -- developed with Rockwell for the Apollo program -- defined the first era of database work. Both systems organized data as linked records navigated by pointer chains: to retrieve a customer's orders, a program had to start at the customer record and physically traverse the linked order records. The "database administrator" in this era was a storage engineer: someone who decided how the chains were laid out on disk, which indexes to maintain, and how to reorganize the physical structure when fragmentation degraded performance. The CODASYL Database Task Group, which Bachman chaired, codified this approach as a standard in 1971. Knowledge was physical and highly specialized; moving data from one machine to another was a major undertaking. The role was real but not yet widely titled.

    Effect on the work

    These systems required deep physical knowledge of storage layouts that could not be delegated to application developers. They created the first genuine demand for a specialist whose value was institutional knowledge of the data -- the foundational labor dynamic of the DBA profession.

  • Relational model and SQL: Codd (1970), System R (1974), Oracle (1979), DB2 (1983)

    Edgar Codd's 1970 paper "A Relational Model of Data for Large Shared Data Banks" proposed organizing data into tables linked by values rather than physical pointers. IBM's System R experiment (1974-1979) at the San Jose Research Laboratory proved it was practical at transaction scale and introduced the Structured Query Language. Oracle Corporation -- then Relational Software Inc. -- shipped the first commercial relational database in 1979, beating IBM's own DB2 by four years. DB2 launched on IBM MVS mainframes in 1983. The relational model fundamentally changed what a DBA did: instead of managing pointer chains, the job became designing logical schemas, writing and tuning SQL, managing the cost-based optimizer, and governing the indexes that made queries run at acceptable speed. The physical storage layer became largely invisible to application developers -- exactly Codd's goal -- which meant DBAs became the arbiters of a more abstract but no less consequential domain.

    Effect on the work

    The relational model grew the total DBA market substantially: the simpler programmer interface meant more applications could use databases, which meant more databases needing administration. At the same time, the logical-vs-physical separation reduced the most arcane physical-storage expertise and shifted DBA value toward SQL mastery and optimizer knowledge.

  • Commercial RDBMS ecosystem: Oracle 6-8i, SQL Server, Sybase, client-server architecture

    The commercial relational database market exploded in the late 1980s and 1990s. Oracle grew from a small startup to a dominant enterprise software company; Microsoft entered the market with SQL Server (developed from a Sybase port) in 1989; Sybase and Informix competed for the UNIX mid-market. The client-server architecture that replaced mainframe time-sharing meant every department could run its own database server, which multiplied the number of database instances -- and DBAs -- required across an enterprise. The Oracle DBA became a recognizable and well-compensated specialization by the early 1990s; Oracle certifications (the Oracle Certified Professional program launched in the mid-1990s) formalized the career path. SQL tuning, Oracle Statspack performance analysis, and tablespace management were the core craft skills of the era. The Y2K remediation effort (1997-1999) temporarily further inflated DBA hiring as organizations paid specialists to audit every date-handling stored procedure and schema in production.

    Effect on the work

    DBA employment roughly tripled between 1985 and 2000, growing as the client-server architecture multiplied database instances across the enterprise. Oracle DBA became one of the most sought-after and highest-paid IT specializations of the 1990s.

  • Open-source databases and the web: MySQL 1995, PostgreSQL 1996, LAMP stack

    MySQL debuted on May 23, 1995, created by Michael Widenius, David Axmark, and Allan Larsson as a lightweight open-source database optimized for web read-heavy workloads. PostgreSQL, derived from the Berkeley INGRES/POSTGRES research lineage, followed in 1996. The LAMP stack (Linux, Apache, MySQL, PHP/Perl/Python) became the default architecture for web applications through the early 2000s, powering Amazon, Facebook, Twitter, YouTube, Wikipedia, and most of the early internet economy. For DBAs, this era introduced a challenge: MySQL's ease of deployment meant many web developers created databases without any DBA involvement at all. The role bifurcated -- Oracle and DB2 enterprise DBAs retained strong labor-market position, while the open-source MySQL ecosystem created a lower-cost tier of database work where developers often served as their own DBAs and formal DBA roles were less common.

    Effect on the work

    Open-source databases reduced the barrier to database deployment, growing the overall number of databases without proportionally growing the number of professional DBA positions. The post-dot-com bust (2001-2004) saw DBA employment contract as startups closed and enterprises reduced IT headcount; the open-source tier absorbed much of the remaining database work at lower pay grades.

  • Cloud managed databases and NoSQL: AWS RDS (2009), MongoDB (2009), DynamoDB (2012)

    Amazon Web Services launched Amazon RDS (Relational Database Service) in October 2009 -- a managed MySQL database that handled provisioning, patching, backups, and replication automatically without any DBA involvement in those tasks. MongoDB 1.0 shipped in November 2009 as a document-oriented NoSQL database that stored data as flexible JSON-like documents rather than rigid relational tables, explicitly designed for web-scale applications where schema changes were frequent. Amazon DynamoDB followed in January 2012. These three products, arriving nearly simultaneously, redrew the boundaries of DBA work. Cloud managed services removed the routine maintenance layer -- the patching schedules, the backup jobs, the replication configuration -- and handed it to the cloud provider. NoSQL systems pushed schema responsibility further toward application developers. DBAs who survived this transition did so by owning what the cloud could not: query optimization, capacity and cost governance, compliance architecture, and the judgment calls around when to use which database technology.

    Effect on the work

    Cloud database services absorbed an estimated significant share of routine DBA tasks that had previously required dedicated headcount. The BLS 2018-28 projection still showed positive DBA growth, suggesting the automation was expanding the addressable database universe faster than it was eliminating individual positions -- but the trend toward fewer DBAs per terabyte of managed data was clearly established.

  • Autonomous databases and AI-native tooling: Oracle ADB (2018), pganalyze, Datadog DBM, GitHub Copilot in SSMS

    Oracle launched the Autonomous Database in 2018, positioning it as a "self-driving, self-securing, self-repairing" system that automated provisioning, monitoring, performance tuning, backup, and patching without human intervention. Oracle CEO Mark Hurd stated publicly that if all databases moved to autonomous, "the number of DBAs would go to zero" -- a claim widely covered and widely disputed. By October 2025, Oracle rebranded the product as AI Database 26ai and reported 66% DBA efficiency gains. Simultaneously, a generation of AI-augmentation tools arrived: pganalyze's Indexing Engine tested hundreds of index combinations automatically; Datadog Database Monitoring detected query regressions against historical baselines without manual watch rotations; GitHub Copilot appeared inside SQL Server Management Studio 22 in 2025 to generate T-SQL from natural language. AWS CloudWatch Database Insights (December 2024) replaced Performance Insights with AI-powered execution plan analysis. The DBA role in 2026 sits at a genuine inflection: autonomous platforms handle the maintenance loop, AI tools accelerate the query-writing and optimization layer, and the remaining human value is concentrated in governance (validating what the machine decided), compliance architecture, incident command, and the cross-organizational judgment calls that no automation system can sign off on.

    Effect on the work

    The combined DBA-plus-Architect category (15-1242 and 15-1243) projects +4% growth, suggesting that autonomous platforms are displacing the routine-ops DBA tier while increasing demand for the architectural judgment tier.

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Database Administrators employment and pay by year

On record since1963
Latest US employment69,990 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$104,620 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook-0.1% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 9 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
198020,000n/aEst., Dataversity Researched contextReport
199058,000n/aEst., Robert Half Researched contextReport
1995n/a$46,000Est., University of Missouri Libraries Researched contextReport
2000108,000$51,990BLS OEWS 2000 Official statisticReport
202185,870$96,710BLS OEWS May 2021 Official statisticReport
202280,520$99,890BLS OEWS May 2022 Official statisticReport
202376,140$101,510BLS OEWS May 2023 Official statisticReport
202473,180$104,620BLS OEWS May 2024 Official statisticReport
202569,990$104,620BLS OEWS May 2025 Official statisticReport
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