Louisville, KY
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 Louisville.
What We Place
Representative roles
Platforms and technologies
Our Approach
This editorial hiring guide starts with sourced Louisville business context. Louisville Metro's economic-development plan identifies health care, manufacturing, transportation and warehousing, professional and technical services, finance, and insurance as key sectors. Hiring teams can use the employer's sector to define the systems and evidence a candidate must understand. 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 Louisville 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
Tie each must-have requirement to a task, system, risk, or deadline. Remove industry preferences that do not change how the person will perform the job. This is planning guidance, not measured local demand.
Editorial industry scenario
A health-sector brief should name the protected data, validation, availability, and user-workflow requirements the person will handle. 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
2,770
BLS publishes a narrower metro employment estimate for the proxy occupation. Screen for adjacent experience that transfers without lowering the production bar. The estimate equals 4.049 jobs per one thousand across the metro workforce.
Employment concentration
0.37 location quotient
Louisville/Jefferson County, KY-IN reports a below-national employment concentration for this proxy occupation. Decide which requirements justify a wider regional or remote search.
Annual wage reference
$77,750 to $163,350
The metro median is 19% below the national Software Developers median. Do not use the gap to discount niche platform or domain experience. BLS reports a $110,270 median for the proxy occupation in Louisville/Jefferson County, KY-IN.
Hiring brief scenarios
These scenarios connect location context to role responsibilities. Use them as prompts to verify with the employer, not as measures of Louisville demand, clients, or candidate supply.
Sourced health care and social assistance context
Growing Louisville Together identifies health care and social assistance as a key Louisville sector and describes health care as the city's largest employer category. 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. Health operations can join consumer, patient, provider, facility, referral, workforce, claims, and finance records with restricted fields and services that continue through ordinary release work.
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. Specify the care or administrative process, user populations, protected records, system boundaries, access reviews, service window, integration recovery, and acceptance owner.
Sourced manufacturing context
Manufacturing appears among Louisville's key sectors, and the city plan connects its advanced manufacturing position to water, utilities, and supply-chain capacity. 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. Plant work may connect products, equipment, materials, quality, shifts, maintenance, suppliers, costs, and customer commitments with limited time for production changes.
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. Define the sites, products, production model, utility or equipment dependencies, quality checks, source systems, outage limits, fallback, and operational sign-off.
Sourced logistics and business services context
Louisville's plan lists transportation and warehousing, professional and technical services, finance, and insurance among the city's key sectors and describes major rail, airport, interstate, and river connections. 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. Partner and freight processes may involve accounts, contracts, orders, warehouses, shipment events, service timers, currencies, financial postings, and data from carriers or customer systems.
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. Map the partner and shipment lifecycle, transaction volumes, system authorities, transport interfaces, exception timing, reconciliation, support coverage, and period-end dependency.
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 protected-data handling, lineage, quality controls, governed access, and reproducible analysis. The healthcare and life-science systems context is an editorial scenario, not a measured claim about Louisville.
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 event timeliness, source reconciliation, pipeline recovery, definitions, and operational reporting. The logistics and distribution operations context is an editorial scenario, not a measured claim about Louisville.
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 protected-data handling, lineage, quality controls, governed access, and reproducible analysis. The healthcare and life-science systems context is an editorial scenario, not a measured claim about Louisville.
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 Louisville 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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