Skills highlighted in blue are preferred key skills
Manage scheduling, dependencies, retries, logging and pipeline monitoring. Diagnose failed jobs and data-quality issues and implement lasting fixes. Improve performance through query tuning, partitioning and efficient compute usage. Bring strong sql and practical cloud or distributed-data engineering experience. Design dependable pipelines from operational sources into analytical platforms. Implement transformations, business rules and reusable data workflows. Use distributed processing when data volume or complexity requires it.
Opportunity for a Enterprise Data Engineer to contribute to a growing technology, analytics, enterprise applications, or sales function.
Client Company