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Senior Data Engineer – Data Quality & Observability | Remote 1789

Softgic · Удалённо · United States

# Senior Data Engineer – Data Quality & Observability | Remote 1789 **Softgic** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Data-Engineering]` `[Data-Quality-Engineering]` `[Data-Observability]` `[Data-Engineer]` `[Data-Platform-Engineering]` `[Senior-Data-Quality-Engineer]` `[Remote-Senior-Data-Engineer-Healthcare]` `[Senior-Data-Engineering]` `[DataOps-Engineer]` --- ### About the Role This is a remote position. Are you passionate about building reliable, scalable data platforms and improving data quality at an enterprise level? We are looking for a Senior Data Engineer – Data Quality & Observability to lead the implementation of a modern data quality framework, enabling engineering teams to detect, monitor, and prevent data issues before they impact business operations. In this role, you'll drive the implementation of engineering-owned data quality practices, operationalize GX Core , establish validation standards, and build observability solutions that improve confidence across reporting, synchronization processes, and operational workflows. Responsibilities - Design and implement a scalable data quality framework across the platform. - Lead the implementation and operationalization of GX Core (Great Expectations) as the primary data validation framework. - Develop and maintain reusable data quality rules using a Rule-as-Code approach. - Create automated validation checks for business-critical datasets and workflows. - Implement data observability, monitoring, alerting, and reporting solutions. - Define and maintain data lineage across key business domains. - Design validation processes for data completeness, accuracy, integrity, consistency, reconciliation, freshness, and anomaly detection. - Integrate data quality validations into CI/CD pipelines and release processes. - Develop dashboards and reports to monitor data quality trends and operational health. - Investigate root causes of recurring data issues and implement preventive solutions. - Collaborate with Data Engineering, Application Engineering, QA, Product, and Support teams to establish ownership and governance for data quality. - Define standards for governance, validation frequency, remediation workflows, and quality metrics. - Continuously improve data quality processes and establish long-term observability best practices. Requisitos - 5+ years of experience as a Data Engineer or in similar data engineering roles. - Strong experience designing and implementing enterprise Data Quality frameworks. - Hands-on experience with GX Core (Great Expectations) or similar tools such as Soda . - Strong SQL skills and experience working with Aurora PostgreSQL and Amazon Redshift . - Experience designing data validation rules, reconciliation processes, and observability solutions. - Experience building and maintaining ETL pipelines and large-scale data workflows. - Strong understanding of data modeling, referential integrity, synchronization, and batch processing. - Experience integrating data validation into CI/CD pipelines. - Experience with Git and engineering best practices such as Rule-as-Code. - Experience building dashboards, alerts, and reporting for operational monitoring. - Strong analytical and problem-solving skills with experience performing root cause analysis. - Experience collaborating with cross-functional engineering teams. - Excellent communication and documentation skills. What We Offer - ? Fully remote position. - ? Opportunity to build enterprise-scale data quality and observability solutions. - ? High-impact role with ownership over data quality strategy and engineering best practices. - ? Collaborative environment working alongside Data Engineering, QA, Product, and Application Engineering teams. - ? Opportunity to work with modern data validation, observability, and cloud data technologies while driving continuous improvement across the platform. Originally posted on Himalayas

Наблюдалась 2026-10-20, впервые 2026-10-19, источник — Himalayas (JSON API).

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