1011 Data Engineering Consultant jobs in Dallas,TX

5+ years of engineering, installation, or startup/commissioning of electrical and mechanical systems experience
Bachelor's degree in Mechanical/Electrical Engineering or 5 years of relevant discipline work experience
High technical competency in either mechanical or electrical or control systems utilized in critical infrastructures, with the capability to learn and be trained in the others.
Understand the design concept and intent by reviewing mechanical or electrical design documentation, including drawings, specifications, vendor submittals and sequences of operations and provide feedback on the same in accordance with Amazon standards for basis of design.
Develop but majorly review commissioning documentation such as test plans, scripts and reports developed by vendors to ensure accuracy in accordance with the design requirements.
U.S Citizenship Required
Ability and willingness to travel domestic and internationally for up to 80% of the year.
Required to provide personal transportation for meetings and job visits away from the office; reimbursed.
Walk job sites in uneven terrain
Work at heights and from ladders
Regularly walk, use hands and fingers, reach with hands and arms, stoop, kneel, crouch or crawl
Lift and/or move up to 49 pounds; and participate in group lifts for 50 pounds or more
Carry objects weighing up to 49 pounds for short distances
Perform physical tasks throughout the day without becoming overly tired
Ability to manipulate small wires and objects easily
Work in a noisy environment
Work in environments that are colder or hotter than a normal office environment

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Principal Engineer - Python API Development

Fidelity Investments

Southlake, TX 76092

~ 36 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.