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Dataops Engineer Jobs in Texas (NOW HIRING)

Data Engineer

Dallas, TX · 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 ...

Implement DataOps practices. (CI/CD, testing, observability, incident response) * Lead and develop ... Education: Bachelor's degree in Computer Science, Engineering, Statistics, or related field.

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

$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 NEX

Houston, TX · 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/DataOps practices including CI/CD, automated testing, infrastructure as code, and monitoring. • Ensure compliance with enterprise data governance, privacy, and regulatory requirements (e.g ...

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 ...

Principal Engineer

Dallas, TX · On-site

$103K - $172K/yr

... DevOps/DataOps practices including CI/CD, automated testing, infrastructure as code, and monitoring. • Ensure compliance with enterprise data governance, privacy, and regulatory requirements (e.g ...

Principal Engineer

Dallas, TX · On-site

$103K/yr

... DevOps/DataOps practices including CI/CD, automated testing, infrastructure as code, and monitoring. • Ensure compliance with enterprise data governance, privacy, and regulatory requirements (e.g ...

Data Engineer

Dallas, TX

$113K - $136K/yr

... DataOps concepts and operating in cross-functional teams that include data engineering personas. * The measures of success for this role include delivering data pipelines with trusted, quality data ...

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Dataops Engineer information

See Texas salary details

$20

$55

$97

How much do dataops engineer jobs pay per hour?

As of Jun 8, 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 does a DataOps engineer do?

A DataOps engineer is responsible for managing and automating data pipelines, ensuring efficient data flow from collection to analysis. They use tools like automation scripts, cloud platforms, and data management frameworks to improve data quality, reliability, and deployment speed in data-driven environments.

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 are popular job titles related to Dataops Engineer jobs in Texas? For Dataops Engineer jobs in Texas, the most frequently searched job titles are:
What cities in Texas are hiring for Dataops Engineer jobs? Cities in Texas with the most Dataops Engineer job openings:

Data Engineer

1 point system

Dallas, TX • On-site

$113K - $136K/yr

Contractor

Posted 15 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 pipelines—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.