Remote job
Data Engineer III
Job details
About this role
Role overview
Design and support enterprise data pipelines, models, and analytical assets that help teams make reliable, informed decisions. This role owns technical delivery for assigned work, contributes to data governance and architecture, and prepares trusted data for AI and machine learning applications.
Responsibilities - Build, test, deploy, and maintain ETL/ELT pipelines, data models, and analytics assets, with attention to performance and reliability. - Translate business definitions and data quality needs into technical specifications, models, controls, and remediation plans. - Maintain metadata catalogs, lineage, glossary definitions, and governance practices for assigned data areas. - Investigate data quality problems, coordinate fixes, and report quality trends and remediation progress. - Prepare governed datasets for AI and machine learning, and integrate approved AI capabilities into data workflows. - Validate AI-supported outputs and monitor accuracy, reliability, latency, and cost while protecting sensitive data.
Requirements - Bachelor’s degree in computer science or a related field, or equivalent relevant experience. - At least five years designing and developing ETL/ELT or business intelligence solutions. - Experience across requirements, design, implementation, testing, deployment, and production support for complex data solutions. - Strong grounding in relational databases, data modeling, data warehousing, data quality, metadata, lineage, and access controls. - Working knowledge of AI and machine learning data needs and integration patterns, such as inference APIs and, where relevant, embeddings or retrieval-augmented generation. - Ability to work independently, solve ambiguous technical problems, communicate clearly with technical and business partners, and follow security and privacy practices.
Nice to have
Experience evaluating AI-enabled workflows with representative data, quality measures, and human review; familiarity with version control, code review, automated testing, deployment, and monitoring for data pipelines and AI integrations.