Senior Data Engineer – Data Quality & Observability

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 PostgreSQL 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.