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Senior Dataops Engineer Jobs (NOW HIRING)

Analytica

Senior Data Engineer

Washington, DC · On-site

$120K - $163K/yr

The ideal candidate possesses deep expertise in cloud-native data engineering, DataOps practices, and regulated healthcare environments. The Senior Data Engineer will lead the development and ...

Orion180 Insurance Co.

Sr. Data Engineer

Irving, TX · On-site

$101K - $138K/yr

We are actively recruiting for a Sr. Data Engineer to join our growing team! We believe ... DataOps & Automation * Establish robust CI/CD deployment pipelines for all data assets using Azure ...

Northern Base

Senior Data Engineer

Dallas, TX · On-site

$104K - $142K/yr

Role Summary We are seeking a highly skilled Senior Data Engineer with expertise in Fivetran ... Follow DataOps, CI/CD, governance, and security best practices. Required Technical Skills Core ...

Analytica

Senior Data Engineer

Washington, DC · On-site

$119K - $162K/yr

The ideal candidate possesses deep expertise in cloud-native data engineering, DataOps practices, and regulated healthcare environments. The Senior Data Engineer will lead the development and ...

Analytica

Senior Data Engineer

Washington, DC · On-site

$120K - $163K/yr

The ideal candidate possesses deep expertise in cloud-native data engineering, DataOps practices, and regulated healthcare environments. The Senior Data Engineer will lead the development and ...

NAVA Software Solutions

Sr Data Developer

Houston, TX · On-site

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 ...

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Senior Dataops Engineer information

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$59.5K

$126.6K

$183.5K

How much do senior dataops engineer jobs pay per year?

As of Sep 13, 2026, the average yearly pay for senior dataops engineer in the United States is $126,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,500.00 and $143,500.00 per year, depending on experience, location, and employer.

What is a senior DataOps engineer?

Senior DataOps Engineers are experienced professionals who design, implement, and manage data pipelines and workflows to ensure reliable, efficient, and scalable data operations within an organization. They bridge the gap between data engineering, DevOps, and analytics by automating data integration, deployment, and monitoring processes. Their role often includes optimizing data infrastructure, ensuring data quality, and enabling data teams to quickly deliver insights. Senior DataOps Engineers also mentor junior team members and help define best practices for data operations.

What are some common challenges a senior DataOps engineer faces when scaling data infrastructure for a growing organization?

A Senior DataOps Engineer often encounters challenges such as ensuring data pipeline reliability during rapid scaling, managing increasing data volume and complexity, and maintaining high data quality across distributed environments. Balancing automation with flexibility, integrating new tools with legacy systems, and coordinating with cross-functional teams (like data scientists and DevOps) are also key hurdles. Success in this role requires proactively identifying bottlenecks, optimizing workflows, and fostering a culture of collaboration to support evolving business needs.

What are the key skills and qualifications needed to thrive as a senior DataOps engineer, and why are they important?

To thrive as a Senior DataOps Engineer, you need a solid background in data engineering, automation, CI/CD pipelines, and strong knowledge of data architecture, usually supported by a degree in computer science or a related field. Expertise in tools like Apache Airflow, Kubernetes, Docker, cloud platforms (AWS, Azure, or GCP), and proficiency with scripting languages such as Python or Bash are typically required, along with certifications like AWS Certified Solutions Architect or Google Cloud Data Engineer. Outstanding problem-solving skills, collaboration, and effective communication are essential soft skills for integrating diverse teams and managing complex workflows. These capabilities ensure data reliability, streamlined operations, and scalable solutions in dynamic data-driven environments.

What is the difference between Senior Dataops Engineer vs Data Engineer?

AspectSenior Dataops EngineerData Engineer
CredentialsTypically requires experience with cloud platforms, scripting, and data pipeline toolsRequires knowledge of database systems, SQL, and data modeling
Work EnvironmentFocuses on deployment, automation, and maintaining data infrastructureDesigns and builds data pipelines and storage solutions
Industry UsageCommon in organizations emphasizing data operations and automationWidespread across industries for data storage and processing

The main difference is that Senior Dataops Engineers focus on managing and automating data workflows and infrastructure, while Data Engineers primarily design and build data pipelines and storage systems. Both roles require strong technical skills, but their focus areas differ within the data ecosystem.

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Senior Dataops Engineer jobs near you
Infographic showing various Senior Dataops Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $126,557 per year, or $60.8 per hour.

Senior Machine Learning Data Engineer (DataOps), Materra

Mountain View, CA • On-site

X, the moonshot factory
1 - 5K employees
Learn more about X, the moonshot factoryexternal

$135K - $162K/yr

Full-time

Re-posted 14 days ago

Key responsibilities

  • Architect and build automated ETL and ELT data pipelines to aggregate, clean, and harmonize data from disparate sources.

  • Implement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection.

  • Design and maintain dataset versioning and storage systems to support machine learning training and ensure reproducibility.

Job description

S e n i o r M a c h i n e L e a r n i n g D a t a E n g i n e e r ( D a t a O p s ) , M a t e r r a
Software Engineering Mountain View, CA
About the team:
Materra is on a mission to radically reduce global waste and move to a true circular economy. The team has developed technology that identifies waste material at the molecular level-starting with plastics. Materra works with industry partners to improve the way recycling centers process plastics using AI and robotics, to make recycling more affordable and scalable.
About the RoleWe are looking for a Senior Machine Learning Data Engineer (DataOps) to build and unify the data infrastructure that powers our model training pipelines. In this role, you will lead the effort to consolidate fragmented data sources into a cohesive, high-quality data foundation.
Your primary focus will be designing automated ingestion pipelines, establishing data quality validation frameworks, and managing dataset versioning to support our machine learning training loops. You will bridge the gap between operations, remote annotation teams, and machine learning engineers to ensure our models are trained on reliable, well-structured data.
Key Responsibilities
  • Architect and build automated ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) data pipelines to aggregate, clean, and harmonize data from disparate sources, databases, and operational ingestion flows.
  • Implement DataOps practices, including data quality monitoring, automated schema validation, and anomaly detection to catch corrupt or mislabeled data early.
  • Standardize and integrate third-party annotation workflows and remote labeling feeds into unified datasets ready for model training.
  • Design and maintain dataset versioning and storage systems to allow reproducible machine learning experiments and seamless data retrieval.
  • Collaborate with machine learning engineers and operations teams to translate raw material, form factor, and sensor metadata into structured training features.

Requirements
  • Education: Degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
  • Data Engineering & Architecture: 5+ years experience building scalable data pipelines, managing relational and non-relational databases, and unifying fragmented data storage systems.
  • Modern Python Proficiency: Expertise in Python and data manipulation libraries (e.g., Pandas, NumPy, or SQL).
  • Data Quality & DataOps: Practical experience implementing automated data validation, quality control frameworks, and dataset versioning practices.
  • ML Data Lifecycle Understanding: Hands-on experience structuring datasets specifically for machine learning workflows, including handling annotations, metadata tracking, and training set curation.

Preferred Skills
  • Google Cloud Ecosystem: Hands-on experience with Google Cloud platform tools (e.g., BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI Data Pipelines).
  • Workflow Orchestration: Experience managing pipelines using Google Cloud Composer or equivalent orchestration frameworks (e.g., Apache Airflow, Prefect, Dagster).
  • Multimodal / Unstructured Data: Experience handling mixed data types, including image datasets, sensor metadata, and unstructured physical property records.
  • Annotation Platform Integration: Familiarity with data labeling platforms, human-in-the-loop workflows, or integrating third-party annotation APIs.
  • Validation & Versioning Tooling: Exposure to data quality and ML versioning tools (e.g., Great Expectations, DVC, or TFX/Data Validation).

The US base salary range for this full-time position is $164,000 - $261,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
An Equal Opportunity Workplace
At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
If you have a disability or special need that requires accommodation, please contact us at x-accommodation-request@x.team .

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