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Data Ops Engineer Jobs in Texas (NOW HIRING)

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · On-site +1

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · On-site

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to ...

Service Experts

Principal Architect

Richardson, TX · On-site

... engineering, analytics, and platform operations teams. • Act as the single architectural and ... Data Operations & Reliability • Own the Data Ops operating model including incident management ...

SpaceX

Data Engineer (Starlink)

Bastrop, TX · On-site

$113K - $136K/yr

DATA ENGINEER (STARLINK) At SpaceX, we're leveraging our experience building rockets and spacecraft ... You will partner with Enterprise sales, channel and reseller operations, sales ops, and enablement ...

SpaceX

Data Engineer (Starlink)

Bastrop, TX · On-site

$113K - $136K/yr

DATA ENGINEER (STARLINK) At SpaceX, we're leveraging our experience building rockets and spacecraft ... You will partner with Enterprise sales, channel and reseller operations, sales ops, and enablement ...

SpaceX

Data Engineer (Starlink)

Bastrop, TX · On-site

$113K - $136K/yr

DATA ENGINEER (STARLINK) At SpaceX, we're leveraging our experience building rockets and spacecraft ... You will partner with Enterprise sales, channel and reseller operations, sales ops, and enablement ...

SpaceX

Data Engineer (Starlink)

Bastrop, TX · On-site

$113K - $136K/yr

The Data Engineer on the Starlink Enterprise team will improve data quality and build integrations ... ops, and operational platforms -- reducing manual re-entry and preventing quality issues at the ...

Esolvit Inc

Sr. Dev. Ops Engineer

Austin, TX · On-site

$128K - $165K/yr

... data and cloud computing. Seamless delivery is ensured by our professionals, through the usage of proven methodologies, consistent practices, management disciplines, and business metrics. ESolvit ...

Perficientcorp Inc

Data Engineer

Fort Worth, TX · On-site

$109K - $131K/yr

... dev ops, product model that includes designing, developing, and implementing large-scale ... data engineering solutions 5-7 years data analytics experience using SQL 5-7 years of cloud ...

Tiger Analytics

Lead Data Engineer - AWS

Dallas, TX · On-site

$113K - $136K/yr

... Senior Data Engineer to join their team. The role focuses on building scalable Generative AI ... Ops: Integrate AWS Step Functions and SageMaker Pipelines to automate the fine-tuning and ...

Guidehouse

AWS Cloud DBA

San Antonio, TX · On-site

Owns discovery of data systems and data ops, ensures smooth and timely handoff of data systems ... Familiarity with DevOps practices and Infrastructure as Code (Terraform, CloudFormation, CDK)

BusPatrol LLC

IT Ops Automation Engineer

Austin, TX · On-site

$70K - $80K/yr

IT Ops Automation Engineer Overview At BusPatrol, we're transforming student transportation safety ... Use data insights to recommend and prioritize automation candidates that reduce manual effort ...

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Showing results 41-60

All JobsData Ops Engineer JobsData Ops Engineer Jobs in Texas

Data Ops Engineer information

See Texas salary details

$41.5K

$120.9K

$165.4K

How much do data ops engineer jobs pay per year?

As of Aug 11, 2026, the average yearly pay for data ops engineer in Texas is $120,851.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,700.00 and $128,100.00 per year, depending on experience, location, and employer.

What is a Data Ops Engineer?

Data Ops Engineers are professionals who bridge the gap between data engineering and operations. They focus on automating, monitoring, and optimizing data pipelines to ensure reliable, efficient, and secure data flow within organizations. Their responsibilities often include managing data integration, workflow orchestration, deployment of data infrastructure, and implementing best practices for data quality and governance. Data Ops Engineers work closely with data scientists, analysts, and IT teams to support data-driven decision-making and maintain high data availability. Their role is crucial in modern organizations that rely on large-scale data processing and analytics.

What are the key skills and qualifications needed to thrive as a Data Ops Engineer, and why are they important?

To thrive as a Data Ops Engineer, you need a solid background in data engineering, automation, and cloud infrastructure, often supported by a degree in computer science or related field. Experience with tools like Apache Airflow, Docker, Kubernetes, CI/CD pipelines, and proficiency in scripting languages such as Python or Bash is typically required. Strong problem-solving skills, attention to detail, and effective communication help you collaborate with data teams and troubleshoot complex data workflows. These skills ensure reliable data delivery, streamlined operations, and scalable solutions that support organizational data goals.

What is the difference between Data Ops Engineer vs Data Engineer?

AspectData Ops EngineerData Engineer
CredentialsCertifications in data management, cloud platforms, scriptingCertifications in data engineering, SQL, cloud services
Work EnvironmentFocus on data pipelines, automation, deployment, and monitoringFocus on data modeling, ETL processes, database design
Industry UsageUsed in organizations emphasizing data operations, automation, and DevOps practicesUsed in data-centric roles focusing on building data infrastructure

While both roles work with data infrastructure, Data Ops Engineers primarily focus on automating and managing data pipelines and deployment processes, whereas Data Engineers concentrate on designing and building data systems. The roles often overlap but differ in their core focus areas and responsibilities.

How does a Data Ops Engineer typically collaborate with data scientists and software engineers within an organization?

Data Ops Engineers play a crucial role in bridging the gap between data science and engineering teams. They ensure smooth data pipeline operations, help automate workflows, and support data scientists by providing reliable, scalable infrastructure. Collaboration often involves participating in cross-functional meetings to understand data requirements, troubleshooting data quality issues, and implementing solutions that enable efficient experimentation and model deployment. This collaborative environment helps facilitate quick iterations and reliable delivery of data products.
What are popular job titles related to Data Ops Engineer jobs in Texas? For Data Ops Engineer jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Data Ops Engineer jobs in Texas look for? The top searched job categories for Data Ops Engineer jobs in Texas are:
Data Ops Engineer jobs near you
Infographic showing various Data Ops Engineer job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $120,851 per year, or $58.1 per hour.

ML Ops Architect

Tiger Analytics Inc.

Dallas, TX • On-site, Remote

Full-time

Re-posted 15 days ago

Job description

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
We are looking for a motivated and passionate Machine Learning Engineers for our team.
Job Description:
As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine capabilities across the organization. You will work closely with internal customers and infrastructure teams to build our next generation data science workbench and ML platform and products. You will be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs. If you have a penchant for creative solutions and enjoy working in a hands-on, collaborative environment, then this role is for you.
Requirements
What you'll do in the role:
  • Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.
  • Deploy and manage machine learning & data pipelines in production environments.
  • Work on containerization and orchestration solutions for model deployment.
  • Participate in fast iteration cycles, adapting to evolving project requirements.
  • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
  • Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
  • Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
  • Manage and monitor machine learning infrastructure, ensuring high availability and performance.
  • Implement robust monitoring and logging solutions for tracking model performance and system health.
  • Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
  • Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
  • Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
  • Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.
  • Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

Basic Qualifications:
  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
  • Typically requires 7+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.
  • At least 3 years of experience designing and building data-intensive solutions using distributed computing.
  • At least 3 years of experience productionizing, monitoring, and maintaining models

Must have skills:
  • Understanding of Azure stack like Azure Machine Learning, Azure Data Factory, Azure Databricks, Azure Kubernetes Service, Azure Monitor, etc.
  • Demonstrated expertise in building and deploying AI/Machine Learning solutions at scale leveraging cloud such as AWS, Azure, or Google Cloud Platform.
  • Experience in developing and maintaining APIs (e.g.: REST).
  • Experience specifying infrastructure and Infrastructure as a code (e.g.: Ansible, Terraform).
  • Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
  • Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g.: Python, Spark, DataBricks, Github, MLFlow, Airflow).
  • Expertise in Unix Shell scripting and dependency-driven job schedulers.
  • Understanding of security and compliance requirements in ML infrastructure.
  • Experience with visualization technologies (e.g.: RShiny, Streamlit, Python DASH, Tableau, PowerBI).
  • Familiarity with data privacy standards, methodologies, and best practices.

Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

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