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Data Ops Engineer Jobs in Texas (NOW HIRING)

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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 ...

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

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 ...

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 ...

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 ...

Apple

Sr. Machine Learning Engineer - Finance

Austin, TX · On-site

$184K - $277K/yr

As a machine learning engineer in Finance, you'll play an integral and global role in building the ... in data ops best practices Experience developing in Python while following DRY principles ...

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 ...

Charles Schwab

AI/ML Ops and Data Engineer

Southlake, TX · On-site

$107K - $129K/yr

... data engineers * Demonstrated support of critical systems in production * Experience partnering with data scientists/MLE/Ops teams to deliver business outcomes Core Responsibilities * Design and ...

Apple

Sr. Machine Learning Engineer - Finance

Austin, TX · On-site

$184K - $277K/yr

As a machine learning engineer in Finance, you'll play an integral and global role in building the ... in data ops best practices Experience developing in Python while following DRY principles ...

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 ...

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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 Sep 5, 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.

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 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.

Is data operations a good career?

Data Operations, often involving roles like Data Ops Engineer, is a growing field focused on managing data pipelines, automation, and infrastructure. It offers strong job demand, competitive salaries, and opportunities to work with tools like cloud platforms and data management systems, making it a viable career choice for those interested in data and technology.

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, 87% Full Time, 10% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $120,851 per year, or $58.1 per hour.

AI/ML Ops and Data Engineer

Charles Schwab Inc.

Southlake, TX • On-site

$134K - $195K/yr

Full-time

Re-posted 13 days ago

Job description

Your Opportunity
At Schwab, you're empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us "challenge the status quo" and transform the finance industry together.
We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).
Hands-on technical lead responsible for taking AI/ML projects from development to production in Google Cloud Platform (GCP). This role owns architecture, implementation, deployment, and operations.
Required Skills
  • Expert-level Google Cloud experience, especially services used for AI/ML use cases (e.g., BigQuery, Vertex AI, GCS, Dataflow, Pub/Sub, Cloud Run/GKE, Composer/Airflow, IAM, Cloud Monitoring/Logging)
  • Expert Python for production-grade data and backend engineering
  • Strong SQL and data modeling for analytics, scalability, and operational workloads
  • Strong CI/CD and containerization skills (Docker, Git workflows, automated testing, release pipelines)
  • Solid cloud security and governance practices (IAM, secrets, least privilege, auditability)
  • Strong observability and reliability engineering skills (monitoring, alerting, incident response, SLAs/SLOs)
  • Fundamental understanding of AI/ML lifecycle/model development needed to productionize AI/ML systems (training/serving integration, model versioning, pipeline monitoring support)

What you have
Required Work Experience
  • 8+ years in data/software engineering, including 2+ years in technical leadership
  • Proven track record delivering production grade AI/ML use cases on GCP or other cloud providers
  • Experience building and operating scalable batch/streaming pipelines
  • Experience leading design reviews, enforcing engineering standards, and mentoring data engineers
  • Demonstrated support of critical systems in production
  • Experience partnering with data scientists/MLE/Ops teams to deliver business outcomes

Core Responsibilities
  • Design and build production-ready AI/ML powered, security related use cases on GCP
  • Lead end-to-end deployment from prototype to production with clear quality gates
  • Understand, document, and lead the resolution of technical debts
  • Implement coding standards, test strategy, data quality checks, alerting mechanisms, and operational runbooks
  • Ensure platform reliability, security, and cost efficiency
  • Mentor the MLOps and data engineers while remaining hands-on in code and delivery

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