Data Engineer

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.