- अनुभव
- कोई
- वेतन
- EUR 56,700 – EUR 69,300 / year
- उद्घाटन
- 1
- की तैनाती
- 5 पहले
- कार्य मोड
- कार्यालय में हूँ
- Eligibility
- Candidates must have a valid work permit. The role is suitable for engineers with experience in automation frameworks, quality engineering practices, data pipelines, and cross-functional collaboration. English proficiency is required, and other languages are considered an advantage.
- Resume
- Required to apply
Where you'll work
नौकरी का विवरण
About RepRisk
RepRisk is a globally recognized Data as a Service company focused on reputational risk and responsible business conduct. The company’s purpose is to increase visibility into business conduct risks and support positive change. Its platform combines advanced AI, deep subject-matter expertise, and a well-established methodology to help clients make faster, better-informed decisions with confidence.
Guided by values such as intellectual honesty, humility, operational excellence, openness, and respect, RepRisk brings together a diverse team of specialists working across offices in Zurich, Toronto, New York, London, Berlin, Manila, and Tokyo. With around 400 employees, the company aims to set the benchmark for business conduct data worldwide.
What You Can Expect
- A diverse, international, mission-driven workplace where your contribution has real impact.
- A collaborative culture that emphasizes openness, respect, and healthy work-life balance.
- Flexible working hours with a hybrid setup that includes home office days.
- Up to 4 weeks per year working from abroad, subject to policy and approval.
- Paid learning and volunteering time, along with charity donation matching.
- Health and fitness support to promote wellbeing.
- Regular team and social activities that connect the global community.
- A welcoming office with free coffee, refreshments, fresh fruit, and healthy snacks.
- An inclusive environment that values diversity and different perspectives.
Role Overview
The Quality Engineer will focus on quality as a property of the overall system rather than just a testing function. The role is suited to someone who is interested in failure patterns, cross-component issues, and building early-warning mechanisms that help teams prevent and detect problems sooner. The position sits within the global Technology division and reports to the Global Head of Quality Engineering in Berlin.
Key Responsibilities
- Design and implement automated validations across data pipelines and services.
- Create reusable automation and AI-supported solutions that strengthen quality practices.
- Develop and refine frameworks and tools that help teams spot, diagnose, and respond to quality issues.
- Work closely with engineering and data teams to improve quality processes, tooling, and developer experience across software and data systems.
Requirements
- Strong working knowledge of Python and SQL; Java knowledge is an advantage.
- Good understanding of data pipelines, transformation flows, and methods for checking data integrity in complex environments.
- Practical experience building, extending, and maintaining automation frameworks or testing tools.
- Experience with CI/CD pipelines such as GitLab or similar tools, including automated quality validations.
- Clear communication skills and strong English proficiency; additional languages are a plus.
- Useful experience includes work in data-intensive, distributed, or platform-based environments.
- Exposure to Databricks or comparable data platforms is beneficial.
- Familiarity with AWS or other cloud providers is an advantage.
- Knowledge of data quality practices and tools such as SODA or Great Expectations is a plus.
- Experience evaluating or validating AI-generated outputs is also helpful.
Additional Information
Only candidates with a valid work permit will be considered for this role.
The salary range for this position is EUR 56,700 to 69,300.
Candidate Profile
This opportunity is best suited to an engineer who sees quality as an engineering discipline, enjoys improving system reliability at scale, and likes collaborating across teams to raise standards for software and data quality.