Senior Data Engineer – Data Quality & Observability

This position is listed on behalf of a partner company, who manages all applications and next steps.

Our partner is looking for a Senior Data Engineer – Data Quality & Observability based in Germany.

This role offers the opportunity to design and strengthen enterprise-scale data quality and observability capabilities within a modern data environment.

You will take ownership of building frameworks that improve trust, reliability, and transparency across critical business data systems.

The position focuses on implementing automated validation, monitoring, and governance practices that prevent data issues before they impact operations.

You will collaborate with engineering, product, quality, and support teams to establish scalable data standards and best practices.

This is a high-impact opportunity for a senior data professional who enjoys solving complex data challenges and driving continuous improvement.

You will help shape the future of data reliability through innovative engineering solutions, automation, and cloud-based technologies.

Accountabilities

The Senior Data Engineer – Data Quality & Observability will lead the design and implementation of scalable data quality frameworks, ensuring enterprise data assets remain accurate, reliable, and actionable.

The role combines technical execution, process improvement, and cross-functional collaboration to establish strong data governance and observability practices.

• Design and implement a scalable enterprise data quality framework across data platforms and business domains.
• Lead the implementation and operationalization of GX Core (Great Expectations) or similar data validation solutions.
• Develop reusable data quality rules using a Rule-as-Code approach.
• Build automated validation checks for critical datasets, workflows, and operational processes.
• Implement data observability solutions, including monitoring, alerting, reporting, and quality dashboards.
• Define and maintain data lineage across key business areas.
• Create validation processes covering data completeness, accuracy, integrity, consistency, reconciliation, freshness, and anomaly detection.
• Integrate data quality checks into CI/CD pipelines and engineering release processes.
• Develop reporting solutions to track data quality trends and operational health metrics.
• Investigate recurring data issues, perform root cause analysis, and implement preventive improvements.
• Partner with Data Engineering, Application Engineering, QA, Product, and Support teams to establish clear ownership and governance practices.
• Define standards for validation frequency, remediation workflows, quality metrics, and long-term observability strategies.
• Continuously improve data engineering practices and promote reliable, scalable data solutions.

Requirements

The ideal candidate is a senior data engineering professional with strong experience building enterprise data platforms, implementing quality frameworks, and improving data reliability through automation and observability.

They should have strong technical expertise, analytical thinking, and the ability to collaborate effectively with multiple engineering teams.

• 5+ years of experience as a Data Engineer or in a similar data engineering role.
• Proven experience designing and implementing enterprise-level data quality frameworks.
• Hands-on experience with GX Core (Great Expectations) or comparable data quality tools such as Soda.
• Strong SQL skills and experience working with databases such as Aurora Postgre SQL and Amazon Redshift.
• Experience designing data validation rules, reconciliation processes, and observability solutions.
• Strong background in building and maintaining ETL pipelines and large-scale data workflows.
• Understanding of data modeling, referential integrity, synchronization processes, and batch processing.
• Experience integrating automated data validation into CI/CD pipelines.
• Familiarity with Git workflows and engineering practices such as Rule-as-Code.
• Experience creating dashboards, monitoring systems, alerts, and operational reporting.
• Strong problem-solving skills with experience conducting root cause analysis.
• Ability to collaborate effectively with cross-functional engineering and business teams.
• Excellent communication, documentation, and knowledge-sharing skills.

Benefits

• Fully remote work opportunity.
• Opportunity to build enterprise-scale data quality and observability solutions.
• High-impact role with ownership over data reliability strategy and engineering standards.
• Collaboration with experienced teams across Data Engineering, QA, Product, and Application Engineering.
• Exposure to modern data validation frameworks, cloud data platforms, and observability technologies.
• Opportunity to drive continuous improvement and influence long-term data engineering practices.

How Jobgether works

We use an

AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements.

Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.

The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR).

You may exercise your rights (access, rectification, erasure, objection) at any time.

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information.

These tools assist our recruitment team but do not replace human judgment.

Final hiring decisions are ultimately made by humans.

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