About the RoleWe 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 EngineeringThis 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 OwnBackend Pipeline MigrationRebuild 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 reviewMigrate scheduled data pipelines (daily, weekly, monthly) to Python on the existing AWS orchestration infrastructure, with Slack-based failure notifications replacing Alteryx Server alertsReplicate all Snowflake read/write workflows currently executed via Alteryx, including deduplication, standardization, and enrichment pipelines — ensuring all writes are idempotent and auditableBuild replacement logic for the full Data Ingestion, Data Update, Data Pull, and Data Count Pull operational task setOwn credential management migration from Alteryx Server to AWS Secrets Manager or Parameter StoreREST API IntegrationsRebuild connectors for CoreSignal Contact Enrichment, PredictLeads (Companies and Technology Lookup), Lastbounce (file upload and result retrieval), and the Jira Iterative connectorImplement OAuth2 flows, pagination handling, and JSON parsing in Python across all external API integrationsRebuild Jira Ticket Breakdown and Data Research Ticket Breakdown outputs using the Jira REST APIScheduled ReportingGenerate 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 siteBuild and maintain the Prod-to-Stage DB Sync and Server metadata update pipelinesSelf-Service Application LayerRebuild 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 toolsBuild parameterized interfaces (dropdowns, file uploads, filter inputs) that match current Alteryx app behavior without requiring users to understand the underlying logicEnsure all app outputs — Excel files, CSV exports, SharePoint uploads, email notifications — are delivered the same way users expect todayImplement role-based access control for the self-service layer, controlling which users and teams can access which applicationsOwn the hosting and deployment model (internal server, S3 static + Lambda, or containerized) in collaboration with the broader engineering teamAgentic Development WorkflowsStructure and direct Claude Code sessions to autonomously scaffold application components — backends, form logic, output handlers — maintaining code quality through rigorous reviewDevelop reusable prompt templates and task decomposition patterns that accelerate delivery as the migration progressesValidate and harden AI-generated code against production requirements before deploymentRequirementsWhat We're Looking ForRequired7+ 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 designREST API experience — OAuth2, pagination, rate limiting, JSON/XML parsingFull-stack capability — Python backend (Flask or FastAPI) with HTML/JS frontend; able to build and ship a working web application end to endAWS 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 contextDemonstrated 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 autocompleteHands-on experience with agentic coding tools — Claude Code, Cursor, Devin, or similar — directing autonomous AI execution for real deliverablesAbility to reverse-engineer undocumented legacy workflows and reproduce their output exactly in a new stack — treating existing outputs as the test oracleGit proficiency — branching, PRs, versioned releasesProduction-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 barStrong PlusMicrosoft Graph API — SharePoint file writes, list operations, and email dispatchSnowflake architecture — beyond querying: has designed table structures, configured roles and grants, managed compute sizing, or used cloning and time-travel in a production warehouseExperience building self-service data tools or internal ops tooling for non-technical usersFamiliarity 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 productionReact or Vue for more complex frontend componentsKNIME Analytics Platform familiarity — relevant for the analyst self-service tool decisionData quality libraries — Great Expectations, phonenumbers, email-validatorExperience decomposing complex engineering problems into prompt sequences for agentic AI executionBenefits Government Mandated Benefits (SSS, Pag-Ibig, Philheath, and 13th month) Health Insurance Standard Leave (Emergency, Sick, and Vacation)
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)
