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Dataops Engineer Jobs in Texas (NOW HIRING)

Data Engineer

Dallas, TX ยท On-site

$113K - $136K/yr

DataOps & Automation: Own the deployment lifecycle using Databricks Asset Bundles (DABs) and CI/CD ... Azure DevOps) to ensure reproducible environments. * Performance Tuning: Optimize streaming ...

Senior Data Developer Location: Houston TX - 2 days/week hybrid Type: Full time/Direct Hire As the ... Leverage tools for DataOps (CI/CD) Requirements * Bachelor's degree in Computer Science, Data ...

Data Engineer Lead

Plano, TX ยท On-site

$98K - $130K/yr

... DataOps practices. Qualifications : Required : โ€ข 8 - 10 Years of experience โ€ข Snowflake โ€ข DBT โ€ข Design and build data pipelines and models using Snowflake and dbt โ€ข Lead a team, provide ...

Data Engineer Lead

Plano, TX ยท On-site

$98K - $130K/yr

... DataOps practices. Qualifications : Required : โ€ข Snowflake โ€ข DBT โ€ข Design and build data ... engineering delivery โ€ข Ability to balance hands-on work with team leadership โ€ข Gather and ...

DevOps/Python Engineer

Austin, TX ยท On-site

$52.25 - $71.50/hr

Apply CI/CD and Infrastructure-as-Code (IaC) principles to data workflows Required Skills & Experience: * 5+ years of experience in DataOps, Data Engineering, DevOps Engineering, or related roles

We are seeking a DataOps Lead to own and advance the operational reliability, quality, and scalability of our cloud-based data platforms . This role sits at the intersection of data engineering ...

We are seeking a DataOps Lead to own and advance the operational reliability, quality, and scalability of our cloud-based data platforms . This role sits at the intersection of data engineering ...

We are seeking a DataOps Lead to own and advance the operational reliability, quality, and scalability of our cloud-based data platforms . This role sits at the intersection of data engineering ...

This role sits within a DataOps team responsible for ensuring data pipelines are accurate, reliable, and delivered on time. You'll work closely with teammates, engineering partners, and operational ...

This role sits within a DataOps team responsible for ensuring data pipelines are accurate, reliable, and delivered on time. You'll work closely with teammates, engineering partners, and operational ...

DevOps Engineer NEX

Houston, TX ยท On-site

$50.25 - $69/hr

Knowledge of security and compliance practices in cloud environments * 5+ years experience in Data Engineering, DataOps, or Data Platform Operations * Ability to understand and speak English at a ...

DevOps Engineer Lead NEX

Houston, TX ยท On-site

$50.25 - $69/hr

Knowledge of security and compliance practices in cloud environments * 5+ years experience in Data Engineering, DataOps, or Data Platform Operations * Ability to understand and speak English at a ...

DevOps Engineer Lead NEX

Houston, TX ยท On-site

$50.25 - $69/hr

Knowledge of security and compliance practices in cloud environments * 5+ years experience in Data Engineering, DataOps, or Data Platform Operations * Ability to understand and speak English at a ...

MLOps Engineer DPR is a leading construction company committed to delivering high-quality ... Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly ...

MLOps Engineer DPR is a leading construction company committed to delivering high-quality ... Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly ...

MLOps Engineer DPR is a leading construction company committed to delivering high-quality ... Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly ...

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Showing results 1-20

Dataops Engineer information

See Texas salary details

$20

$55

$97

How much do dataops engineer jobs pay per hour?

As of Jul 5, 2026, the average hourly pay for dataops engineer in Texas is $55.85, according to ZipRecruiter salary data. Most workers in this role earn between $40.38 and $61.52 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Dataops Engineer position, and why are they important?

To thrive as a Dataops Engineer, you need a strong background in data engineering, automation, CI/CD practices, and cloud platforms, typically supported by a degree in computer science or a related field. Familiarity with tools like Jenkins, Docker, Kubernetes, Terraform, and major cloud providers (AWS, Azure, GCP) as well as relevant certifications significantly enhances effectiveness in this role. Strong problem-solving skills, collaboration, and clear communication are essential soft skills for working across teams and addressing fast-changing data needs. These combined abilities ensure smooth data pipeline operations, minimize downtime, and enable efficient, reliable delivery of data-driven solutions.

What are the common day-to-day responsibilities of a Dataops Engineer?

A Dataops Engineer is typically responsible for designing, deploying, and maintaining automated data pipelines that support business analytics and operations. Daily tasks often include monitoring data workflows, troubleshooting pipeline issues, optimizing system performance, and collaborating with data scientists, analysts, and DevOps teams to ensure seamless data delivery. You may also be involved in implementing data quality checks, managing cloud resources, and improving deployment processes. This role is dynamic and fast-paced, requiring both technical expertise and effective cross-team communication. Working as a Dataops Engineer provides the opportunity to work on cutting-edge projects and directly influence data-driven decision-making across the organization.

What is a DataOps Engineer job?

A DataOps Engineer is responsible for streamlining and automating data workflows, ensuring data quality, and enabling efficient data integration across platforms. They work closely with data scientists, analysts, and engineers to implement CI/CD pipelines, manage data infrastructure, and optimize data delivery processes. Their role involves leveraging tools for orchestration, monitoring, and version control to enhance collaboration and reliability in data operations.

What are the most commonly searched types of Dataops Engineer jobs in Texas? The most popular types of Dataops Engineer jobs in Texas are:
What job categories do people searching Dataops Engineer jobs in Texas look for? The top searched job categories for Dataops Engineer jobs in Texas are:
What cities in Texas are hiring for Dataops Engineer jobs? Cities in Texas with the most Dataops Engineer job openings:
Infographic showing various Dataops Engineer job openings in Texas as of June 2026, with employment types broken down into 5% As Needed, 38% Full Time, 3% Part Time, 30% Temporary, 1% Contract, and 23% Nights. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $116,159 per year, or $55.8 per hour.
Data Engineer

Data Engineer

1 point system

Dallas, TX โ€ข On-site

$113K - $136K/yr

Contractor

Posted 12 days ago


Job description

Role Summary
We are seeking a high-performing Data Engineer to design and implement a real-time data platform using the Medallion Architecture. You will be responsible for the end-to-end development of data pipelinesโ€”from ingesting real-time source data into Bronze, transforming it into a relational silver layer, and finally delivering high-concurrency, consumption-ready JSON Gold tables. You will act as a "Full Stack" data professional, handling everything from infrastructure automation (DataOps) to complex nested data modeling.
Key Responsibilities

  • Real-Time Ingestion: Build scalable ingestion pipelines using Auto Loader and Spark Structured Streaming to capture data from Kafka, Event Hubs, or CDC sources into raw Delta tables.
  • Relational Transformation: Develop ELT logic to cleanse, deduplicate, and normalize data into a relational format. Ensure ACID compliance and "exactly-once" processing semantics.
  • JSON API Optimization: Design and build the layer specifically for client consumption. This involves flattening/nesting data into optimized JSON structures within Delta tables to support low-latency API queries.
  • Advanced Orchestration: Implement and manage complex workflows using Delta Live Tables (DLT) or Standard Streaming Live tables and Databricks Workflows to ensure data freshness and lineage.
  • Governance & Security: Use Unity Catalog to enforce fine-grained access control (row/column level) and maintain a searchable data catalog for consuming clients.
  • DataOps & Automation: Own the deployment lifecycle using Databricks Asset Bundles (DABs) and CI/CD pipelines (GitHub Actions/Azure DevOps) to ensure reproducible environments.
  • Performance Tuning: Optimize streaming triggers, watermarking, and stateful processing to minimize latency and manage cloud costs effectively.