About the Role

We are hiring two Full-Stack Developers, Data Applications, who will develop the backend data pipelines and self-service application layer.

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

Our team operates in an AI-assisted development model. We use Claude (Anthropic) as an active co-author across the full engineering lifecycle, code generation, agentic task execution, architectural review, and documentation. This is not optional tooling. It is how we move fast with a lean team against a hard deadline.

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, phonenumbers, email-validator
Experience decomposing complex engineering problems into prompt sequences for agentic AI execution

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