Data Engineer
Description Data Engineer Data Platform & Engineering - Build and maintain reliable data pipelines using tools such as Airbyte, Snowflake, and dbt. - Design and optimize scalable data models that support analytics, reporting, search, and AI-powered applications. - Develop and maintain dbt models, testing frameworks, documentation, and deployment processes. - Ensure data quality, reliability, governance, and operational excellence across the data platform. - Take direction from Senior staff to learn and support standard work and best practices. AI Application Development - Build and enhance AI-powered applications that securely retrieve, interpret, and summarize business information. - Design retrieval, context-management, prompt-engineering, and tool-calling approaches that improve accuracy, transparency, and user trust. - Develop agent-based workflows that can complete multi-step business processes using approved tools and systems. - Create APIs, services, and integrations that connect AI applications with internal platforms and business systems. - Gain an understanding of business use of the AI agents and provide feedback for ensuring valuable results to users. Operations & Collaboration - Monitor system health, application performance, data quality, AI usage, and operational costs. - Troubleshoot issues across data pipelines, warehouse transformations, software services, and AI workflows. - Apply security, access control, testing, logging, and governance best practices throughout the development lifecycle. - Partner with business stakeholders to translate requirements into scalable and maintainable solutions. - Leverage AI-assisted development tools to improve productivity while maintaining high standards for code quality, testing, and documentation. What You Bring? Required Qualifications - Experience in software engineering, data engineering, analytics engineering, or a related technical discipline. - Strong SQL skills and proficiency in at least one programming language, preferably Python. - Experience building and supporting cloud-based data warehouses and production data pipelines. - Hands-on experience with Snowflake and dbt. - Experience with data integration or ingestion tools such as Airbyte. - Experience developing applications that use large language models (LLMs), retrieval-augmented generation (RAG), tool calling, or agent-based workflows. - Familiarity with AI-assisted software development tools such as Claude Code, GitHub Copilot, Cursor, Codex, or similar platforms. - Understanding of APIs, source control, automated testing, CI/CD, and modern software engineering practices. - Strong problem-solving skills with the ability to troubleshoot across interconnected systems. - Effective communication skills and the ability to collaborate with both technical and business stakeholders. Preferred Qualifications - Experience with Snowflake Cortex or other enterprise AI/LLM platforms. - Experience designing semantic layers, metadata systems, or governed retrieval architectures. - Familiarity with LLM evaluation frameworks, observability tools, token-cost optimization, and response quality monitoring. - Experience implementing role-based access controls, data governance standards, and data privacy protections. - Experience supporting internal applications from early-stage prototypes through enterprise-scale production adoption. #LI-NL1 #LI-Remote