Dallas, TX
Data Architect recruiting based on accountable delivery experience. Crosscheck recruits Data, Cloud & Security candidates for contract, contract-to-hire, and permanent roles tied to Dallas.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Dallas business context. Dallas Economic Development lists finance, AI and semiconductors, data centers, advanced manufacturing, aerospace, and logistics among its target industries. The mix creates several valid technical hiring scenarios, each with different operating evidence. A Data Architect search should define the operating boundary before comparing resumes. The brief must distinguish Data Architect, Enterprise Data Architect, Cloud Data Architect and connect role-specific scope to the work this person will personally own. Screening centers on data domains, models, integration patterns, governance, lineage, access, platform standards, migration plans, and architecture decisions.
Define the systems, delivery stage, operating boundary, and ownership expected from the Data Architect
Screen candidates for evidence of data domains, models, integration patterns, governance, lineage, access, platform standards, migration plans, and architecture decisions
Separate direct delivery experience from adjacent product, project, or consulting exposure
Support contract, contract-to-hire, and permanent searches across the US and Canada
Start the search
Include the business context, systems, delivery phase, work model, compensation, and interview timeline. A Crosscheck search lead will use that context to calibrate the role before sourcing begins.
Local Market Brief
Record the required decisions, systems, delivery stage, and support duties for Data Architect work. Treat Data Modeling, Data Domains, Data Mesh, Warehousing as context for the assignment, not a keyword checklist. Separate that scope from adjacent Analytics Architect, Data Integration Architect, Data Solution Architect responsibilities so each candidate is evaluated against the same completed brief. Build the calibration map from the actual assignment: Data Architect against Data Modeling and Data Domains; Enterprise Data Architect against Data Mesh and Warehousing; Cloud Data Architect against Lakehouse and Integration Patterns; Analytics Architect against Metadata and Architecture Governance; Data Integration Architect against data domains and models; Data Solution Architect against integration patterns and governance. For the delivery handoff, trace the working sequence from Integration Patterns to Lakehouse to Warehousing to Data Mesh to Data Domains to Data Modeling and name who accepts each boundary. The three sourced Dallas contexts below turn that scope into intake and screening decisions. They do not measure current vacancies, candidate supply, or Crosscheck client activity.
Editorial market scenario
Set the system boundary, decision rights, work model, and interview schedule before sourcing. Candidates can then compare the role on concrete responsibilities. This is planning guidance, not measured local demand.
Editorial industry scenario
A finance-facing brief should identify the transaction, reporting, audit, privacy, and availability requirements attached to the role. Confirm that this context applies to the employer before using it in the search.
Screening focus
Screening covers data scale, platform choices, governance needs, and the candidate's record of making data useful to downstream teams.
Published labor benchmark
BLS does not publish an occupation matching Data Architect. Crosscheck uses Computer Systems Analysts (15-1211) as the closest published broad benchmark; it is not a count or pay estimate for this exact specialty.
BLS OEWS May 2025, published May 15, 2026
Published metro employment
20,000
BLS publishes a large metro employment estimate for the proxy occupation, but the figure covers many employers, seniority levels, and specializations outside Data Architect work. The estimate equals 4.938 jobs per one thousand across the metro workforce.
Employment concentration
1.48 location quotient
Dallas-Fort Worth-Arlington, TX reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$75,850 to $170,020
The metro median is 16% above the national Computer Systems Analysts median. Test whether the role's scope and location requirement support that difference. BLS reports a $122,520 median for the proxy occupation in Dallas-Fort Worth-Arlington, TX.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Dallas demand, clients, or candidate supply.
Sourced financial services and fintech context
Dallas Economic Development lists financial services and fintech among the sectors it targets for growth and recruitment. Its industry material also describes Dallas as a major financial employment center, which supports a finance-systems scenario without proving role-level demand. Define how Lakehouse, Integration Patterns, Metadata, Architecture Governance fit the employer's current environment. Ask which constraints changed the design, what Data Architect owned directly, who approved the decision, and how the result was checked after delivery. Finance platforms require controlled access, complete transaction records, reconciliations, and releases that respect reporting calendars.
Evidence to request: Request a redacted design, configuration, test, runbook, review record, or operating measure that supports the candidate's account of Data Architect ownership. State the product, accounting or risk process, control owner, and close or reporting deadline attached to the role.
Sourced ai, semiconductors, and data centers context
Dallas Economic Development groups AI, semiconductors, and data centers within its technology targets. Those activities span software, physical infrastructure, capacity planning, and systems that support design or production work. Set the boundary for ownership checkpoints before interviews. A useful account involving data domains, models, integration patterns, governance names the starting condition, alternatives considered, implementation sequence, failure handling, and the operating team that received the work. Compute-heavy environments can impose capacity limits, hardware dependencies, energy constraints, and maintenance windows that shape software design.
Evidence to request: Use a comparable scenario involving platform standards, migration plans, and architecture decisions, Data Architect and score assumptions, technical judgment, communication, delivery steps, and the evidence proposed for acceptance. Separate experience running cloud software from experience with data-center operations, semiconductor workflows, or systems close to physical equipment.
Sourced aerospace, manufacturing, and logistics context
Dallas Economic Development also targets advanced manufacturing, aviation, defense, aerospace, transportation, and logistics. A technical search in that setting may support physical assets, regulated supply chains, warehouses, or field operations. Connect adjacent role boundaries to an employer decision rather than a broad tool list. Require the candidate to explain work with lineage, access, platform standards, migration plans, including dependencies, controls, measurable evidence, and responsibility when the original plan changed. Asset-based operations can require serial traceability, supplier integration, controlled maintenance records, and support across several facilities.
Evidence to request: Ask for a problem involving Enterprise Data Architect responsibilities. Record the signal, diagnosis, decision, corrective action, handoff, and verification the candidate personally completed. Define whether the candidate needs sector knowledge, site experience, export-control awareness, or a record of supporting distributed operations.
Interview scorecard
Ask each candidate the same core questions. Score the evidence, ownership, and judgment in the answer instead of relying on job-title or keyword matches.
1. Data Architect: Data Modeling
Choose a Data Modeling decision from your work as Data Architect. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess lineage, data quality, governed reporting, and recovery for business-critical pipelines. The finance and insurance systems context is an editorial scenario, not a measured claim about Dallas.
2. Enterprise Data Architect: Data Domains
Describe project work you completed as Enterprise Data Architect involving Data Domains that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess lineage, data quality, governed reporting, and recovery for business-critical pipelines. The finance and insurance systems context is an editorial scenario, not a measured claim about Dallas.
3. Cloud Data Architect: Data Mesh
For a Data Mesh system you supported, explain the handoff, operating limits, and measures used after launch. Where did your responsibility begin and end?
Use the answer to assess lineage, data quality, governed reporting, and recovery for business-critical pipelines. The finance and insurance systems context is an editorial scenario, not a measured claim about Dallas.
Screened for data domains, models, integration patterns, governance, lineage, access, platform standards, migration plans, and architecture decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Data Modeling, Data Domains, Data Mesh to a concrete hiring responsibility.
Show how Data Modeling, Data Domains, Data Mesh shaped one delivery decision. Which constraint mattered, and what did the candidate own?
Evidence check: Look for an artifact, test, configuration record, or operating measure that supports the account. Compare it with work such as technical product and platform teams.
Screened for data domains, models, integration patterns, governance, lineage, access, platform standards, migration plans, and architecture decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Warehousing, Lakehouse, Integration Patterns to a concrete hiring responsibility.
Where did Enterprise Data Architect work involving Warehousing, Lakehouse, Integration Patterns fail or change direction? What evidence prompted the correction?
Evidence check: A useful answer names the failure signal, the candidate's decision, and the result. Certification alone does not establish project ownership.
Screened for data domains, models, integration patterns, governance, lineage, access, platform standards, migration plans, and architecture decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Metadata, Architecture Governance, data domains to a concrete hiring responsibility.
Explain the handoff and operating boundary for a project using Metadata, Architecture Governance, data domains. Who approved changes, monitored results, and supported the system?
Evidence check: Request documentation, controls, or production measures that distinguish direct ownership from observation or team-level credit.
Screened for data domains, models, integration patterns, governance, lineage, access, platform standards, migration plans, and architecture decisions, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects models, integration patterns, governance to a concrete hiring responsibility.
Which tradeoff would change the design of models, integration patterns, governance for this hiring task: support contract, contract-to-hire, and permanent searches across the us and canada?
Evidence check: Score the response on technical judgment, stated assumptions, and evidence from comparable work rather than vocabulary coverage.
Organizations hiring across Dallas can use the market context below to shape location, compensation, and screening requirements for Data Architect searches.
Technical product and platform teams
Transformation and implementation programs
Internal engineering and operations teams
Systems integration and advisory teams
FAQ
For qualified exclusive searches in our core disciplines, Crosscheck targets a first candidate slate within 48 hours after a completed intake. Contract placements include a 40-billable-hour replacement guarantee, and permanent placements include a 90-day replacement guarantee, subject to the signed agreement.
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