Data Engineers build and operate the systems that move data from sources to the people, applications, analyses, and models that depend on it. They ingest data, transform it, organize it for use, and keep it accurate, secure, traceable, and available at scale. Their work turns fragmented files, documents, databases, APIs, and event streams into reliable data products. AI is one important consumer of that work, alongside reporting, visualization, analytics, software, and operational systems. Some openings may involve preparing dependable data for machine learning, document retrieval, or AI evaluation. The center of the role remains dependable data engineering from source to use. What you may build includes ingestion and transformation pipelines for batch, streaming, and event-driven data from APIs, databases, files, documents, object stores, messaging systems, and operational platforms; cloud and on-premises data platforms, including databases, data lakes, warehouses, lakehouses, and serving layers for reporting, visualization, software, analytics, and other operational uses; quality, metadata, lineage, provenance, and access-control capabilities that make data trustworthy, explain how it changed, and keep its use within approved boundaries; and the operational layer around data products: orchestration, testing, monitoring, backfills, replay, recovery, retention and deletion implementation, performance and cost tuning, infrastructure as code, and technical documentation. Where the work requires it, versioned feature, training, testing, or evaluation datasets; document and ret

Data Engineers build and operate the systems that move data from sources to the people, applications, analyses, and models that depend on it. They ingest data, transform it, organize it for use, and keep it accurate, secure, traceable, and available at scale. Their work turns fragmented files, documents, databases, APIs, and event streams into reliable data products. AI is one important consumer of that work, alongside reporting, visualization, analytics, software, and operational systems. Some openings may involve preparing dependable data for machine learning, document retrieval, or AI evaluation. The center of the role remains dependable data engineering from source to use. What you may build includes ingestion and transformation pipelines for batch, streaming, and event-driven data from APIs, databases, files, documents, object stores, messaging systems, and operational platforms; cloud and on-premises data platforms, including databases, data lakes, warehouses, lakehouses, and serving layers for reporting, visualization, software, analytics, and other operational uses; quality, metadata, lineage, provenance, and access-control capabilities that make data trustworthy, explain how it changed, and keep its use within approved boundaries; and the operational layer around data products: orchestration, testing, monitoring, backfills, replay, recovery, retention and deletion implementation, performance and cost tuning, infrastructure as code, and technical documentation. Where the work requires it, versioned feature, training, testing, or evaluation datasets; document and ret