Full-Stack Developer, Data Applications

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.

What You'll Own

Backend Pipeline Migration

• Rebuild 22 shared Alteryx macros as a versioned Python shared library — Git-controlled, unit-tested, and importable across all new scripts — using Claude Code for accelerated generation and review
• Migrate scheduled data pipelines (daily, weekly, monthly) to Python on the existing AWS orchestration infrastructure, with Slack-based failure notifications replacing Alteryx Server alerts
• Replicate all Snowflake read/write workflows currently executed via Alteryx, including deduplication, standardization, and enrichment pipelines — ensuring all writes are idempotent and auditable
• Build replacement logic for the full Data Ingestion, Data Update, Data Pull, and Data Count Pull operational task set
• Own credential management migration from Alteryx Server to AWS Secrets Manager or Parameter Store

REST API Integrations

• Rebuild connectors for CoreSignal Contact Enrichment, PredictLeads (Companies and Technology Lookup), Lastbounce (file upload and result retrieval), and the Jira Iterative connector
• Implement OAuth2 flows, pagination handling, and JSON parsing in Python across all external API integrations
• Rebuild Jira Ticket Breakdown and Data Research Ticket Breakdown outputs using the Jira REST API

Scheduled Reporting

• Generate Excel output files (openpyxl / xlsxwriter) and push to SharePoint via Microsoft Graph API for all 10+ weekly and monthly reports currently delivered to the Data Ops SharePoint site
• Build and maintain the Prod-to-Stage DB Sync and Server metadata update pipelines

Self-Service Application Layer

• Rebuild 40+ Alteryx Gallery applications as Python-backed web apps (Flask or FastAPI backends, Next.js/react— starting with EFO Data Pull variants, Quotes App, Look-a-Like apps, and Data Research validation tools
• Build parameterized interfaces (dropdowns, file uploads, filter inputs) that match current Alteryx app behavior without requiring users to understand the underlying logic
• Ensure all app outputs — Excel files, CSV exports, SharePoint uploads, email notifications — are delivered the same way users expect today
• Implement role-based access control for the self-service layer, controlling which users and teams can access which applications
• Own the hosting and deployment model (internal server, S3 static + Lambda, or containerized) in collaboration with the broader engineering team

Agentic Development Workflows

• Structure and direct Claude Code sessions to autonomously scaffold application components — backends, form logic, output handlers — maintaining code quality through rigorous review
• Develop reusable prompt templates and task decomposition patterns that accelerate delivery as the migration progresses
• Validate and harden AI-generated code against production requirements before deployment

Requirements

What We're Looking For

Required

• 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
• 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

Benefits

• Government Mandated Benefits (SSS, Pag-Ibig, Philheath, and 13th month)
• Health Insurance
• Standard Leave (Emergency, Sick, and Vacation)