Full-Stack Developers - Data Applications
Summary
Builds and maintains production-grade data pipelines and self-service web apps in Python/Snowflake, replacing legacy Alteryx workflows for internal teams, with AI tools like Claude as active co-authors for coding, architecture, and documentation.
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.
Requirements
- 7+ years of Python development — pandas, requests, openpyxl, regex as daily tools; comfortable owning a production codebase end to end
- Solid Snowflake SQL — joins, CTEs, window functions, write operations (INSERT, MERGE, TRUNCATE/INSERT)
- Data pipeline architecture — proven ability to design a pipeline from scratch: choose the right processing model (batch vs. event-driven), select appropriate AWS services, and defend those decisions; has produced architecture decisions that were adopted by a team, not just implemented someone else's design
- 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 data pipeline architecture — hands-on experience selecting and configuring AWS services for a data workload from scratch: Lambda, Step Functions or Glue for orchestration, ECS/Fargate or EC2 for execution, S3 for storage, Secrets Manager for credential management, and EventBridge for scheduling; can justify which service to use and why for a given context
- 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
- Ability to reverse-engineer undocumented legacy workflows and reproduce their output exactly in a new stack — treating existing outputs as the test oracle
- Git proficiency — branching, PRs, versioned releases
- Production-scale pipeline experience — has owned a data pipeline serving multiple internal or external consumers, running on a defined schedule with SLA implications, and has debugged it in production; small or solo projects do not meet this bar
- Strong Plus
- Microsoft Graph API — SharePoint file writes, list operations, and email dispatch
- Snowflake architecture — beyond querying: has designed table structures, configured roles and grants, managed compute sizing, or used cloning and time-travel in a production warehouse
- 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 — Airflow, Prefect, or AWS Step Functions; has built and maintained DAGs with task dependencies, retry logic, and failure alerting in production