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.

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