Full Stack Developer (HYBRID) Python, AWS

We are looking for 2 Sr. Python Full-Stack Developer / Data Applications Engineer with 5 years of Python development experience, strong expertise in Snowflake and REST APIs , solid AWS fundamentals , and proven experience using AI-assisted and agentic coding tools such as Claude Code, Cursor, or GitHub Copilot in a production environment.

The developers will work across the same surface: Python pipelines, Snowflake workflows, REST API integrations, and web-based self-service applications that replaces the 40+ Alteryx Gallery applications used daily by five internal teams.

Requirements

• 5 years of Python development — pandas, requests, openpyxl, regex as daily tools
• Solid Snowflake SQL — joins, CTEs, window functions, write operations (INSERT, MERGE, TRUNCATE/INSERT)
• REST API experience — OAuth2, pagination, rate limiting, JSON/XML parsing
• Full-stack capability — Python backend (Flask or FastAPI) with HTML/JS frontend; able to build and ship a working web application end to end
• AWS fundamentals — S3 read/write, Lambda or EC2 execution, Secrets Manager or Parameter Store
• Demonstrated experience with AI-assisted development — using LLMs (Claude, Copilot, GPT-4, or equivalent) as active co-authors in a production engineering context, not just for autocomplete
• Hands-on experience with agentic coding tools — Claude Code, Cursor, Devin, or similar — directing autonomous AI execution for real deliverables
• Comfort working from existing workflow documentation to rebuild logic in a new stack
• Git proficiency — branching, PRs, versioned releases

Strong Plus:

• Microsoft Graph API — SharePoint file writes, list operations, and email dispatch
• Experience building self-service data tools or internal ops tooling for non-technical users
• Familiarity with Alteryx Designer (understanding what you're replacing is a meaningful head start)
• Workflow orchestration tools — Airflow, Prefect, Step Functions, or similar
• React or Vue for more complex frontend components
• KNIME Analytics Platform familiarity — relevant for the analyst self-service tool decision
• Data quality libraries — Great Expectations, phone numbers, email-validator
• Experience decomposing complex engineering problems into prompt sequences for agentic AI execution