Full Stack Python Developer - Data & AWS

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.

How We Work — AI-Assisted Engineering

This team builds with AI, not alongside it.

We use Claude (Anthropic) as an active co-author across the full development lifecycle, prompt-driven code generation, agentic task execution via Claude Code, UI scaffolding, test generation, and documentation. This is not optional tooling. It is how we compress delivery timelines and maintain quality with a lean team against a hard December deadline.

We are looking for engineers who have already worked this way — who know how to write precise prompts, decompose complex tasks for agentic execution, validate AI-generated code critically, and iterate quickly when outputs miss the mark. Prior experience shipping production code using AI-assisted workflows is a meaningful differentiator, not a nice-to-have.

Requirements

Required

• 5+ 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
• 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