863 Data Engineer Staffing jobs in Frisco,TX

Sr. Data Engineer

McKesson

Irving, TX 75039

$141,336-$184,400/yr

New, Posted 1 day ago

Recommended

DiversityJobs

Software Engineer (Agentic AI/Data Engineering)

Gartner

Irving, TX 75039

Apply Directly

Applied Early Career Program - Field Service Engineer

Applied Materials

Dallas, TX 75221

Paid Relocation to Dallas, TX

Requires TravelUrgently Hiring

Associate degree, recent college graduate, military technical training, trade certification, or equivalent hands-on experience
Basic mechanical aptitude and interest in technical systems
Willingness to learn and read electrical and mechanical schematics
Ability to diagnose and solve basic technical problems
Strong written and verbal communication skills
Basic working knowledge of Microsoft Excel, Word, and PowerPoint
Valid driver's license and ability to obtain a passport, if required for travel
Ability to meet on-site safety, environmental, and customer requirements

SmartExplore AI is experimental.

New, Posted 1 day ago

Recommended

DiversityJobs

Data Engineer - Production Support

Gartner

Irving, TX 75039

New, Posted 7 hours ago

Recommended

Apply Directly

Business Intelligence Data Engineer

Golden State Foods

Frisco, TX 75034

New, Posted 1 day ago

Recommended

DiversityJobs

Site Reliability Engineer

Optimum Communications

Plano, TX 75086

Apply Directly

Principal Engineer - Python API Development

Fidelity Investments

Irving, TX 75061

~ 32 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.

SmartExplore AI is experimental.

Apply Directly

Manufacturing Engineer

DEAN TECHNOLOGY

Addison, TX 75001

~ 18 min OnsiteHealth InsuranceRetirement Benefit

Education: BSEE/BSME/BSMechE and 5+ years' experience, or the equivalent of 10 years' experience in manufacturing engineering, with electronics background.
Skills / Technical Knowledge / Experience: Working knowledge of mechanical assemblies, tooling, fixtures, tolerances, and assembly drawings.
Skills / Technical Knowledge / Experience: Requires knowledge of fabrication processes, material specification, and general industrial operations.
Skills / Technical Knowledge / Experience: Must be a business minded engineer to provide continuous improvements to products and processes.
Skills / Technical Knowledge / Experience: Must be proficient with program management tools and have the ability to manage and prioritize multiple projects, tasks, and assignments at once.
Must be eligible to work in the United States without sponsorship.

SmartExplore AI is experimental.

Apply Directly

Principal Engineer - Python API Development

Fidelity Investments

Trophy Club, TX 76262

OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems
A track record of building production‑grade services, libraries, and internal platforms
Linux fluency and scripting are required
Cloud platform leadership (AWS) —hands-on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS—and experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities; exposure to Azure or GCP is beneficial
DevOps and CI/CD at scale, owning standards for automated build/test/deploy (e.g., Jenkins, Git‑based workflows), containerization (Docker), release governance, and multi‑environment promotion for ML‑enabled workloads
Infrastructure as Code (CloudFormation, Terraform/OpenTofu) and platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming), inference routing, traffic management, performance tuning, observability, and controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers, are a backbone of code review across repos, and routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability—driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models; define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection, prediction‑quality monitoring and uncertainty signals, and automated diagnostics/ explainability
Lead cross‑platform incident response and post‑mortems, drive systemic fixes, and evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates that simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap
The base salary range for this position is $107,000-216,000 USD per year.
Base salary is only part of the total compensation package. Depending on the position and eligibility requirements, the offer package may also include bonus or other variable compensation.