NSXNational Skills ExchangeSign in
Back to Framework

Data Warehousing Specialists

SOC 15-1243.01Job Zone 4 · Considerable Preparationv.26.05

Context coveredThis framework covers data warehousing practice in enterprise and industry sector environments, spanning ETL design, database architecture, data quality, standards governance, and platform leadership calibrated to Job Zone 4 preparation and experience.

Sources:O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.SkillsCrosswalkSkillsCrosswalk — O*NET enrichment + adjacency index. Opens in new tab.
Emerging
Entry / Apprentice
  1. Data warehouse process models — identify and document sourcing, loading, transformation, and extraction steps under direct supervision in a structured enterprise environment.
  2. Warehouse data quality — perform basic verification checks on structure and accuracy using predefined validation scripts in a team-supported data environment.
  3. Data mapping documentation — assist in mapping data fields between source systems and data warehouses following established templates and guidelines.
  4. Data extraction procedures — execute existing ETL scripts from administration or billing systems under senior specialist direction in a production support setting.
  5. Warehouse database structures — assist in designing simple schemas by applying foundational relational database principles in a supervised project environment.
  6. Data warehouse standards — review and apply existing organizational naming conventions and structural standards when working with warehouse elements.
  7. Troubleshooting support — document and escalate data warehouse incidents by following established triage protocols in a helpdesk-supported environment.
  8. Programming tasks — modify small, well-defined segments of existing ETL or reporting code using current languages under code-review oversight.
  9. Metadata management software — navigate and query metadata repositories to locate data lineage information under guided instruction in a warehouse environment.
  10. Technical documentation — read and interpret system specifications, data dictionaries, and warehouse design documents to support assigned task completion.
Developing
Mid-level / Established
  1. Data warehouse process models — design and refine ETL process flows covering sourcing, transformation, and loading with reduced oversight in an enterprise data environment.
  2. Warehouse data quality — conduct systematic accuracy and structural audits using query tools and profiling software, resolving common anomalies independently.
  3. Data mapping specifications — produce and validate end-to-end field mapping documents between source systems, data warehouses, and data marts for routine integration projects.
  4. Data extraction procedures — develop and implement extraction routines from billing, claims, or administrative systems using current ETL platforms in a multi-source environment.
  5. Warehouse database structures — design and deploy normalized and dimensional database schemas to meet defined business requirements in a managed data warehouse setting.
  6. Warehouse standards maintenance — update and enforce data architecture naming conventions, model standards, and tooling guidelines across assigned warehouse components.
  7. Troubleshooting coordination — diagnose and resolve moderate data warehouse failures by analyzing logs, tracing data flows, and coordinating fixes with application teams.
  8. Program development — write and test new ETL programs or reporting scripts using object-oriented or procedural languages to satisfy defined customer requirements.
  9. Systems analysis — evaluate source system data structures and integration points to identify compatibility issues before warehouse load cycles begin.
  10. Stakeholder communication — explain data warehouse processes and integration decisions clearly to business analysts and project managers in cross-functional meetings.
Proficient
Senior / Expert IC
  1. Data warehouse process models — architect comprehensive end-to-end process models spanning sourcing, transformation, loading, and extraction layers across complex, multi-domain enterprise environments.
  2. Data quality assurance — design and execute advanced validation frameworks that detect structural defects, referential integrity failures, and semantic inconsistencies across the full warehouse scope.
  3. Cross-system data mapping — lead the creation of authoritative mapping specifications integrating heterogeneous source systems, enterprise data warehouses, and subject-area data marts.
  4. Extraction procedure engineering — engineer robust, high-performance data extraction solutions from diverse operational systems, incorporating error handling and incremental load strategies.
  5. Warehouse database architecture — design scalable star and snowflake schemas, partitioning strategies, and indexing plans optimized for analytical query performance in large-scale environments.
  6. Standards governance — develop and enforce enterprise-wide data warehouse design standards covering architectures, models, tooling selection, and database nomenclature.
  7. Troubleshooting leadership — independently diagnose and resolve complex, non-routine data warehouse incidents including performance degradation, data corruption, and pipeline failures.
  8. Advanced programming — write, optimize, and refactor complex ETL programs and stored procedures using current languages and technologies to meet evolving customer and system requirements.
  9. Systems evaluation — assess warehouse platform performance, scalability, and fitness-for-purpose against business objectives, recommending architectural improvements based on evidence.
  10. Critical problem solving — apply deductive and inductive reasoning to resolve ambiguous data integration challenges that span multiple business domains and technology stacks.
Advanced
Lead / Principal / Executive
  1. Enterprise data warehouse strategy — define and drive the long-term vision, roadmap, and investment priorities for data warehousing capabilities across the organization.
  2. Process model governance — establish organizational standards for warehouse process model design, ensuring consistency, reusability, and alignment with enterprise data strategy.
  3. Data quality policy — develop and institutionalize data quality policies, metrics, and accountability structures that govern warehouse accuracy and trustworthiness at organizational scale.
  4. Architectural leadership — lead the design of next-generation warehouse and data platform architectures, incorporating cloud, lakehouse, or hybrid patterns to meet strategic business needs.
  5. Standards body leadership — chair or lead enterprise data governance committees that define, publish, and evolve data warehouse standards, taxonomies, and architectural principles.
  6. Organizational troubleshooting capability — build and mature a warehouse support function, including runbooks, escalation frameworks, and on-call structures that ensure platform reliability.
  7. Technology evaluation and adoption — evaluate emerging warehouse platforms, ETL tools, and metadata management solutions, making adoption recommendations with measurable business impact.
  8. Talent development — mentor and coach data warehousing specialists across all experience levels, designing learning pathways that elevate team capability and bench strength.
  9. Cross-functional alignment — partner with executive stakeholders, data governance officers, and enterprise architects to translate business strategy into actionable data warehousing initiatives.
  10. Innovation and research — lead proof-of-concept initiatives exploring advanced data integration, real-time warehouse patterns, or AI-augmented ETL processes to sustain competitive organizational advantage.

AI-at-Work Competency Framework

Sources:Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab.Jadhav & Danve, 2026Skill Automation Feasibility Index — Jadhav & Danve, 2026 (arXiv:2604.06906). Opens in new tab.WEF Skills TaxonomyWEF Skills Taxonomy 2021 — Building a Common Language for Skills at Work. Opens in new tab.

A 4-level framework describing how a worker at each mastery level uses, directs, and evaluates AI tools in this occupation. Each statement cites its evidence inline. Subscribe for $19.99/mo to read the full framework — or sign in if you have a subscription.

  1. Emerging
  2. Developing
  3. Proficient
  4. Advanced
Monthly subscription · Stripe-hosted · unlocks AI-at-Work and Pathsmith™ Durable Skills across the full library

Pathsmith™ Durable Skills Framework

Sources:Pathsmith™ Durable SkillsPathsmith™ Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Subscriber feature

Ten durable-skill domains mapped to four proficiency levels for this occupation. Subscribe to unlock the full Pathsmith™ Durable Skills Framework across all 1,016 occupations.

Monthly subscription · Stripe-hosted · unlocks AI-at-Work and Pathsmith™ Durable Skills across the full library
Show O*NET source anchors52 anchors · skillscrosswalk.com

O*NET enrichment · skillscrosswalk.com

Suggest an O*NET correction

Source anchors that ground each statement

Related titles
Analytics Manager · Big Data Engineer · Data Integrity Specialist · Data Management Engineer · Data Management Manager · Data Management Specialist · Data Migration Specialist · Data Quality Analyst · Data Specialist · Data Storage Specialist · Data Warehouse Analyst · Data Warehouse Developer
RAPIDS apprenticeships
—
O*NET skills
Reading ComprehensionCritical ThinkingProgrammingComplex Problem SolvingSystems AnalysisActive ListeningJudgment and Decision MakingSpeakingSystems EvaluationWritingCoordinationMathematicsActive LearningSocial Perceptiveness
Knowledge domains
Computers and ElectronicsMathematicsEnglish LanguageDesign
Abilities
Written ComprehensionInformation OrderingDeductive ReasoningInductive ReasoningOral ComprehensionNear VisionSpeech RecognitionCategory FlexibilityOral ExpressionWritten Expression
Work styles
Attention to DetailDependabilityIntellectual CuriosityCautiousnessIntegrityAchievement Orientation
Technology
Metadata management softwareDevelopment environment softwareObject or component oriented development softwareData base management system softwareData base user interface and query softwareStorage networking softwareInformation retrieval or search softwarePortal server softwareFile versioning softwareOperating system software
Tasks · seed anchors for statements
  1. Develop data warehouse process models, including sourcing, loading, transformation, and extraction.
  2. Verify the structure, accuracy, or quality of warehouse data.
  3. Map data between source systems, data warehouses, and data marts.
  4. Develop and implement data extraction procedures from other systems, such as administration, billing, or claims.
  5. Design and implement warehouse database structures.
  6. Develop or maintain standards, such as organization, structure, or nomenclature, for the design of data warehouse elements, such as data architectures, models, tools, and databases.
  7. Provide or coordinate troubleshooting support for data warehouses.
  8. Write new programs or modify existing programs to meet customer requirements, using current programming languages and technologies.
CIP education codes
11.010111.010311.040111.050111.070111.080211.090211.100314.090114.090314.270130.700130.709952.1201

Sources: O*NET v30.2 (CC BY 4.0), SkillsCrosswalk.com, LER.me®, Anthropic Economic Index, SAFI (Jadhav & Danve, 2026), WEF Skills Taxonomy 2021, Pathsmith™ Durable Skills Framework. © 2026 EBSCOed.