Teams at RADcube LLC

Build and maintain observability across enterprise data pipelines by implementing data quality, freshness, volume, and schema monitoring. Configure tooling such as Monte Carlo, Great Expectations, CloudWatch, and Datadog; create alerts, maintain SLAs and runbooks, and perform incident triage and root cause analysis. The role requires AWS data services, Python, SQL, and data quality framework expertise.

Build and maintain observability across enterprise data pipelines, including data quality checks, freshness, volume, schema monitoring, alerting, incident triage, root cause analysis, SLA management, and runbooks. Configure tools such as Monte Carlo, Great Expectations, CloudWatch, and Datadog to improve pipeline reliability and data trust.

Designs and builds AWS data ingestion pipelines for structured and unstructured data supporting analytics and AI/GenAI use cases. Responsibilities include developing data lake ingestion frameworks, preparing AI-ready datasets through document parsing, chunking, and embedding pipelines, and creating reusable metadata-driven solutions. Requires AWS services, Python, and PySpark experience, with Bedrock, vector databases, and OpenSearch as preferred skills.