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Data Operations Engineer Jobs (NOW HIRING)

As a Data Operations Developer, your key area of responsibility will be designing, building, and optimizing automated end-to-end data pipelines (ETL/ELT) while ensuring data quality and governance.

As a Data Operations Developer, your key area of responsibility will be designing, building, and optimizing automated end-to-end data pipelines (ETL/ELT) while ensuring data quality and governance.

Data Ops Engineer VFDE

San Diego, CA · On-site

$121K - $146K/yr

SAIC is seeking a Data Operations Engineer to design, build, and maintain real-time data ingestion pipelines. The role involves ensuring reliable data flow from various sources into the data platform ...

Data Ops Engineer VFDE

San Diego, CA · On-site +1

$200K - $240K/yr

ORA_ON_SITE Description We are seeking a Data Operations Engineer to design, build, and maintain real-time data ingestion pipelines. In this role, you will be responsible for the reliable flow of ...

The Data Operations Lead will manage the execution and scaling of the company's data operations ... Work with engineering to ship tooling improvements, track operational metrics, and identify gaps ...

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Data Operations Engineer information

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How much do data operations engineer jobs pay per year?

As of Jun 26, 2026, the average yearly pay for data operations engineer in the United States is $85,029.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,500.00 and $94,000.00 per year, depending on experience, location, and employer.

What does a data operations engineer do?

A data operations engineer manages and maintains data pipelines, ensuring data is collected, processed, and stored efficiently for analysis. They often work with tools like SQL, Python, and cloud platforms, focusing on data quality, automation, and system reliability to support data-driven decision-making.

What is the difference between Data Operations Engineer vs Data Analyst?

AspectData Operations EngineerData Analyst
Required CredentialsBachelor's in CS, Data Science, or related; certifications like AWS, AzureBachelor's in Statistics, Math, or related; certifications like Microsoft Data Analyst
Work EnvironmentData engineering teams, cloud platforms, data pipelinesBusiness units, reporting tools, data visualization platforms
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing firms, finance, consulting, retail
Common Search & Comparison IntentUnderstanding technical differences, job roles, skillsData analysis tasks, reporting, insights generation

The Data Operations Engineer focuses on building and maintaining data infrastructure, pipelines, and ensuring data quality, often working with cloud platforms and scripting. In contrast, a Data Analyst primarily interprets data, creates reports, and provides insights to support business decisions. While both roles work with data, their core responsibilities and skill sets differ significantly.

What engineers make $300,000 a year?

Senior data operations engineers, especially those with extensive experience, advanced skills in data management tools, and certifications, can earn $300,000 or more annually. High compensation is often associated with roles in large organizations, specialized expertise, or leadership positions in data engineering and related fields.

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

To thrive as a Data Operations Engineer, you need a solid understanding of data management, ETL processes, and database systems, typically supported by a degree in computer science or a related field. Familiarity with tools like SQL, Python, data warehousing platforms, and cloud services (AWS, Azure, or GCP) as well as certifications such as AWS Certified Data Analytics are often required. Strong problem-solving, attention to detail, and effective communication skills help you manage complex data workflows and collaborate with cross-functional teams. These skills ensure data integrity, optimize performance, and enable seamless data-driven decision-making across the organization.

Is AI replacing data engineers?

AI is automating certain tasks within data engineering, such as data cleaning and pipeline management, but it does not replace the need for data engineers. Data engineers are essential for designing, building, and maintaining complex data systems, and their expertise in tools like SQL, Python, and cloud platforms remains critical in managing data workflows and ensuring data quality.

What engineers make $500,000?

Senior data operations engineers with extensive experience, advanced skills in data management, automation, and cloud platforms can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires specialized certifications, leadership roles, and a strong track record of impactful projects.

What are some common challenges faced by Data Operations Engineers when working with large-scale data pipelines?

Data Operations Engineers often encounter challenges such as maintaining data quality, ensuring pipeline reliability, and managing system scalability as data volumes grow. Troubleshooting failures in real-time data flows and coordinating with data engineering and analytics teams to address bottlenecks are also common tasks. Additionally, adapting to evolving technologies and implementing automation for routine maintenance can be demanding but are crucial for efficient operations.
More about Data Operations Engineer jobs
What states have the most Data Operations Engineer jobs? States with the most job openings for Data Operations Engineer jobs include:
What job categories do people searching Data Operations Engineer jobs look for? The top searched job categories for Data Operations Engineer jobs are:
Infographic showing various Data Operations Engineer job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 90% Full Time, 8% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.
Data Operations Engineer - Minneapolis, MN

Data Operations Engineer - Minneapolis, MN

Datasite LLC

Minneapolis, MN • On-site

$119K - $143K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 22 days ago


Job description

Datasite and its associated businesses are the global center for facilitating economic value creation for companies across the globe. From data rooms to AI deal sourcing

and more. Here you'll find the finest technological pioneers: Datasite, Blueflame AI, Grata, and Sherpany. They all, collectively, define the future for business growth.

Apply for one position or as many as you like. Talent doesn't always just go in one direction or fit in a single box. We're happy to see whatever your superpower is and find the best place for it to flourish.

Get started now, we look forward to meeting you..

Job Description:

As a Data Operations Engineer at Datasite, you own the full lifecycle of partner data as it moves through our systems - ingestion, transformation, validation, and reconciliation - bringing the monitoring and SLA discipline that sophisticated partners expect. You balance partner trust, engineering velocity, and long-term data platform health while enabling intelligent, contract-driven data exchange across our partner ecosystem.

You bring hands-on experience with modern data tooling (Snowflake, dbt, Airflow, schema registries) paired with practical, AI-augmented workflows that compress manual investigation into minutes. You will help ensure new partnerships are delivered on a foundation of trustworthy data, with the rigor and creative problem solving that lets the broader engineering team stop firefighting and start building.

How We Work Together

Strategic Data Leadership
  • Guide data architecture decisions that incorporate AI-augmented capabilities into ingestion, transformation, and reconciliation workflows for partner integrations.

  • Partner with Product, Engineering, and partner teams to develop flexible data roadmaps aligned to Datasite strategy while adapting to fast-evolving partner data needs.

  • Drive pipeline improvements that scale across diverse partner data formats, reduce operational overhead, and improve reliability of SLA-bound data products.

  • Maintain adaptable data contracts and schema strategies, enabling rapid onboarding of new partners in uncertain, high-velocity environments.

Cross-Team Collaboration & Influence
  • Identify and drive cross-platform improvements (schema registries, validation tooling, data contracts, lineage tracking) that enhance partner and developer experiences.

  • Collaborate across Engineering, Product, and partner teams to deliver AI-first, integration-ready data solutions.

  • Communicate complex data concepts clearly, translating pipeline design trade-offs and SLA commitments for diverse stakeholders.

  • Provide technical guidance that ensures alignment, simplicity, and consistency across data flows and partner integrations.

Problem Solving & Overcoming Obstacles
  • Evaluate trade-offs across freshness, accuracy, latency, and cost, especially in partner-driven and AI-augmented data workflows.

  • Simplify pipelines and drive down data debt while supporting rapid experimentation and onboarding of new partners.

  • Own ambiguous data challenges - mismatched schemas, silent failures, partial loads, reconciliation gaps - and drive them to resolution.

  • Apply strong diagnostics to identify root causes of data discrepancies and deliver resilient, auditable solutions.

Mentorship & Growth
  • Mentor engineers and analytics contributors through coaching and feedback, including adoption of modern and AI-augmented data practices.

  • Support team growth by promoting continuous learning, experimentation, and adaptability in data engineering methods.

  • Foster a culture of psychological safety, collaboration, and shared ownership of data quality.

  • Help raise the bar in hiring, ensuring alignment with Datasite's technical and cultural expectations.

Ownership & Accountability
  • Own end-to-end design and delivery of ingestion pipelines, transformation layers, reconciliation processes, and partner-facing data products.

  • Build pipelines with strong observability, alerting, and self-healing characteristics - so issues are identified and, where possible, remediated before they become partner-visible.

  • Track progress, manage risk, and adapt plans while maintaining a bias for action and high-quality execution.

  • Ensure new partnerships are delivered with care, reliability, and ingenuity, balancing speed with long-term data integrity.

What We're Looking For
  • Strong experience designing and operating data pipelines with defined latency, freshness, and accuracy SLAs

  • Expert SQL skills and proven ability to work with large, complex datasets across diverse partner schemas

  • Hands-on experience with modern data tooling such as Snowflake, dbt, Airflow, and schema registries

  • Practical, in-the-workflow use of agentic tooling to accelerate schema mapping, anomaly detection, data profiling, and pipeline debugging

  • Track record of building monitoring, alerting, runbooks, and reconciliation processes for systems with external commitments

  • Ability to ramp quickly on new partner ecosystems, data formats, and domains

  • Proven success leading work in ambiguous, fast-moving environments

  • Excellent collaboration, communication, and cross-team influence

Work Location & Flexibility

  • This role follows a hybrid work model and is open to candidates based near our Minneapolis office. Employees are expected to work on-site a minimum of two days per week.

The base salary range represents the estimated low and high end for this position based on a good faith assessment of the role and market data at the time of posting. Consistent with applicable law, each candidate's compensation offer may vary and will be determined based on but not limited to, your geographic region, skills, qualifications, and experience along with the requirements of the position. This position may be eligible for bonuses, commissions, or overtime if applicable. Benefits include health insurance (medical, dental, vision), a retirement savings plan, paid time off, and other employee benefits. Specific details will be provided during the interview process. Datasite reserves the right to modify this pay range at any time.

$99,000.00 - $172,700.00

Our company is committed to fostering a diverse and inclusive workforce where all individuals are respected and valued. We are an equal opportunity employer and make all employment decisions without regard to race, color, religion, sex, gender identity, sexual orientation, age, national origin, disability, protected veteran status, or any other protected characteristic. We encourage applications from candidates of all backgrounds and are dedicated to building teams that reflect the diversity of our communities.