DataOps Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

DataOps Engineer Vitória, Espírito Santo WHO WE ARE At Trustly, we're building a smarter, faster, and more secure financial future by revolutionizing the world of payments. As a global leader in Open Banking Payments, we are establishing Pay by Bank as the new standard at checkout, providing unparalleled freedom, speed, and ease to millions of consumers and merchants worldwide. Our Ambition: To build the world's most disruptive payment network and redefine what the payment experience should feel like. Trustly is a global team of innovators, collaborators, and doers. If you are driven by a strong sense of purpose and thrive in a dynamic, entrepreneurial, and high-growth environment, join us and be part of a team that's transforming the way the world pays. About the team Trustly's DataOps team is responsible for delivering the data generated by the application to interested areas, as well as data from APIs and other tools. All this thought in a safe, structured, scalable and generic way, because we work with multiple environments (in different regions) and we need to maintain consistency. We work with both the batch layer (using Airflow) and the streaming layer (Kafka). We help areas in process automation to deliver data more quickly and reliably. We are also concerned with the quality of the data (Data Quality), creating an observability layer for the data to act in a preventive and immediate way to the inconsistencies and failures of our processes. We also work on the delivery of products and services to facilitate

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