Denver, CO
Data Platform Engineer recruiting based on accountable delivery experience. Crosscheck recruits Data, Cloud & Security candidates for contract, contract-to-hire, and permanent roles tied to Denver.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Denver business context. Denver's 2024 workforce plan gives hiring teams a dated public-sector view of the regional economy. It separates professional and technical services, health care, and business occupations instead of treating technology hiring as one market. A Data Platform Engineer search should define the operating boundary before comparing resumes. The brief must distinguish Data Platform Engineer, Senior Data Platform Engineer, Data Infrastructure Engineer and connect role-specific scope to the work this person will personally own. Screening centers on shared data infrastructure, self-service tooling, ingestion frameworks, governance controls, observability, reliability, capacity, and developer experience.
Define the systems, delivery stage, operating boundary, and ownership expected from the Data Platform Engineer
Screen candidates for evidence of shared data infrastructure, self-service tooling, ingestion frameworks, governance controls, observability, reliability, capacity, and developer experience
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 Platform Engineer work. Treat Snowflake, Databricks, BigQuery, Kafka as context for the assignment, not a keyword checklist. Separate that scope from adjacent Data Reliability Engineer, Data Platform Lead, Data Developer Experience Engineer responsibilities so each candidate is evaluated against the same completed brief. Build the calibration map from the actual assignment: Data Platform Engineer against Snowflake and Databricks; Senior Data Platform Engineer against BigQuery and Kafka; Data Infrastructure Engineer against Airflow and Infrastructure as Code; Data Reliability Engineer against Data Observability and Platform APIs; Data Platform Lead against shared data infrastructure and self-service tooling; Data Developer Experience Engineer against ingestion frameworks and governance controls. For the delivery handoff, trace the working sequence from Infrastructure as Code to Airflow to Kafka to BigQuery to Databricks to Snowflake and name who accepts each boundary. The three sourced Denver 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
List the production decisions the hire must own. Use those decisions to assess candidates whose prior title or industry differs from the opening. This is planning guidance, not measured local demand.
Editorial industry scenario
A cross-industry brief should start with the systems, users, risks, and outcomes behind the job title. 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 Platform Engineer. Crosscheck uses Software Developers (15-1252) 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
27,010
BLS publishes a large metro employment estimate for the proxy occupation, but the figure covers many employers, seniority levels, and specializations outside Data Platform Engineer work. The estimate equals 16.783 jobs per one thousand across the metro workforce.
Employment concentration
1.55 location quotient
Denver-Aurora-Centennial, CO reports an above-national employment concentration for this proxy occupation. Confirm current availability through the active search.
Annual wage reference
$99,700 to $209,300
The metro median sits within five percent of the national Software Developers median. Validate the budget against seniority, scope, and current salary data. BLS reports a $137,610 median for the proxy occupation in Denver-Aurora-Centennial, CO.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Denver demand, clients, or candidate supply.
Sourced professional and technical services context
Denver Workforce Development lists professional, scientific, and technical services among the three sectors forecast to add the most jobs from 2024 through 2028. The plan also names computer and mathematical occupations among the occupation families with the most projected growth. Define how Airflow, Infrastructure as Code, Data Observability, Platform APIs fit the employer's current environment. Ask which constraints changed the design, what Data Platform Engineer owned directly, who approved the decision, and how the result was checked after delivery. A search tied to consulting or technical services may cross several client systems, delivery methods, and security boundaries.
Evidence to request: Request a redacted design, configuration, test, runbook, review record, or operating measure that supports the candidate's account of Data Platform Engineer ownership. Confirm whether the hire owns one product, serves several internal teams, or works across client environments before setting the experience bar.
Sourced health care operations context
The same Denver plan includes health care and social assistance in its three fastest-growth sectors for 2024 through 2028. That broad sector covers employers with clinical, claims, workforce, finance, and compliance systems, but the plan does not identify demand for a specific technical role. Set the boundary for ownership checkpoints before interviews. A useful account involving shared data infrastructure, self-service tooling, ingestion frameworks, governance controls names the starting condition, alternatives considered, implementation sequence, failure handling, and the operating team that received the work. Health-related systems can introduce protected data, audit records, uptime requirements, and long approval paths.
Evidence to request: Use a comparable scenario involving capacity, and developer experience, Data Platform Engineer, Senior Data Platform Engineer and score assumptions, technical judgment, communication, delivery steps, and the evidence proposed for acceptance. Ask which data classification, access review, and change-control rules apply to the actual system rather than assuming a standard health care environment.
Sourced business and financial work context
Denver's workforce analysis places business and financial occupations and management occupations alongside computer and mathematical work among the occupation families with the most projected growth. The grouping supports a search brief that connects technical delivery with finance or operating ownership. Connect adjacent role boundaries to an employer decision rather than a broad tool list. Require the candidate to explain work with observability, reliability, capacity, and developer experience, including dependencies, controls, measurable evidence, and responsibility when the original plan changed. Business systems work often requires traceable approvals, reconciled records, and a clear handoff between technical and functional owners.
Evidence to request: Ask for a problem involving Senior Data Platform Engineer responsibilities. Record the signal, diagnosis, decision, corrective action, handoff, and verification the candidate personally completed. Name the business process, control owner, and evidence required at acceptance so candidates can describe comparable work.
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 Platform Engineer: Snowflake
Choose a Snowflake decision from your work as Data Platform Engineer. Which constraint changed the design, and what evidence supported the result?
Use the answer to assess the source systems, scale, data contract, quality rules, consumers, and recovery expectations. The cross-industry technical work context is an editorial scenario, not a measured claim about Denver.
2. Senior Data Platform Engineer: Databricks
Describe project work you completed as Senior Data Platform Engineer involving Databricks that did not follow the original plan. What did you own, and how did you correct it?
Use the answer to assess the source systems, scale, data contract, quality rules, consumers, and recovery expectations. The cross-industry technical work context is an editorial scenario, not a measured claim about Denver.
3. Data Infrastructure Engineer: BigQuery
For a BigQuery 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 the source systems, scale, data contract, quality rules, consumers, and recovery expectations. The cross-industry technical work context is an editorial scenario, not a measured claim about Denver.
Screened for shared data infrastructure, self-service tooling, ingestion frameworks, governance controls, observability, reliability, capacity, and developer experience, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Snowflake, Databricks, BigQuery to a concrete hiring responsibility.
Show how Snowflake, Databricks, BigQuery 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 shared data infrastructure, self-service tooling, ingestion frameworks, governance controls, observability, reliability, capacity, and developer experience, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Kafka, Airflow, Infrastructure as Code to a concrete hiring responsibility.
Where did Senior Data Platform Engineer work involving Kafka, Airflow, Infrastructure as Code 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 shared data infrastructure, self-service tooling, ingestion frameworks, governance controls, observability, reliability, capacity, and developer experience, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects Data Observability, Platform APIs, shared data infrastructure to a concrete hiring responsibility.
Explain the handoff and operating boundary for a project using Data Observability, Platform APIs, shared data infrastructure. 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 shared data infrastructure, self-service tooling, ingestion frameworks, governance controls, observability, reliability, capacity, and developer experience, with the boundary set by the employer's systems, delivery stage, and operating model. The evaluation connects self-service tooling, ingestion frameworks, governance controls to a concrete hiring responsibility.
Which tradeoff would change the design of self-service tooling, ingestion frameworks, governance controls 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 Denver can use the market context below to shape location, compensation, and screening requirements for Data Platform Engineer 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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